Challenges and Future of AI Search: Reliability Issues in Information Retrieval with LLM-Generated Search

 

Case Overview and Innovations

In recent years, AI-powered search (GenAI search) has emerged as a major innovation in information retrieval. Large language models (LLMs) integrate data and knowledge to facilitate Q&A and decision-making, representing a significant upgrade for search engines. However, challenges such as hallucinations and controllability modulation hinder their widespread reliable application. Tech giants like Google are actively exploring generative AI search to enhance competitiveness against products from OpenAI, Perplexity, and others.

A study conducted by the Tow Center for Digital Journalism at Columbia University analyzed the accuracy and consistency of eight GenAI search tools in news information retrieval. The results revealed that current systems still face severe issues in source citation, accurate responses, and the avoidance of erroneous content generation.

Application Scenarios and Performance Analysis

GenAI Search Application Scenarios

1. News Information Retrieval: Users seek AI-powered search tools to quickly access news reports, original article links, and key insights.

2. Decision Support: Businesses and individuals utilize LLMs for market research, industry trend analysis, and forecasting.

3. Knowledge-Based Q&A Systems: AI-driven solutions support specialized domains such as medicine, law, and engineering by providing intelligent responses based on extensive training data.

4. Customized general artificial intelligence experience: Improve the reliability and security of any generated artificial intelligence application by providing the most relevant paragraphs from unified enterprise content sources.

5. Chatbot & Virtual Assistant: Improve the relevance of your chatbot and virtual assistant answers, and make your user experience personalized and content-rich dialogue.

6. Internal knowledge management: Empower employees through personalized and accurate answers based on enterprise knowledge, reduce search time and improve productivity.

7. Customer-oriented support and case transfer: Provide accurate self-help answers based on support knowledge to minimize upgrades, reduce support costs and improve customer satisfaction.

Performance and Existing Challenges

• Inability to Reject Incorrect Answers: Research indicates that AI chatbots tend to provide speculative or incorrect responses rather than outright refusing to answer.

• Fabricated Citations and Invalid Links: LLM-generated URLs may be non-existent or even fabricated, making it difficult for users to verify information authenticity.

• Unstable Accuracy: According to the Tow Center's study, a test involving 1,600 news-based queries found high error rates. For instance, Perplexity had an error rate of 37%, while Grok 3's error rate reached a staggering 94%.

• Lack of Content Licensing Optimization: Even with licensing agreements between AI providers and news organizations, the issue of inaccurate AI-generated information persists.

The Future of AI Search: Enhancing Reliability and Intelligence

To address the challenges LLMs face in information retrieval, AI search reliability can be improved through the following approaches:

1. Enhancing Fact-Checking and Source Tracing Mechanisms: Leveraging knowledge graphs and trusted databases to improve AI search capabilities in accurately retrieving information from credible sources.

2. Introducing Explainability and Refusal Mechanisms: Implementing transparent models that enable LLMs to reject uncertain queries rather than generating misleading responses.

3. Optimizing Generative Search Citation Management: Refining LLM strategies for URL and citation generation to prevent invalid links and fabricated content, improving traceability.

4. Integrating Traditional Search Engine Strengths: Combining GenAI search with traditional index-based search to harness LLMs' natural language processing advantages while maintaining the precision of conventional search methods.

5. Domain-Specific Model Training: Fine-tuning AI models for specialized industries such as healthcare, law, and finance to mitigate hallucination issues and enhance application value in professional settings.

6. Improving Enterprise-Grade Reliability: In business environments, GenAI search must meet higher reliability and confidence thresholds. Following best practices from HaxiTAG, enterprises can adopt private deployment strategies, integrating domain-specific knowledge bases and trusted data sources to enhance AI search precision and controllability. Additionally, establishing AI evaluation and monitoring mechanisms ensures continuous system optimization and the timely correction of misinformation.

Conclusion

While GenAI search enhances information retrieval efficiency, it also exposes issues such as hallucinations, citation errors, and lack of controllability. By optimizing data source management, strengthening refusal mechanisms, integrating traditional search technologies, and implementing domain-specific training, AI search can significantly improve in reliability and intelligence. Moving forward, AI search development should focus on "trustworthiness, traceability, and precision" to achieve truly efficient and secure intelligent information retrieval.

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Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations

This research report, 《Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations》, authored by the Anthropic team, presents a systematic analysis of AI usage patterns in economic tasks by leveraging privacy-preserving data from millions of conversations on Claude.ai. The study aims to provide empirical insights into how AI is integrated into different occupational tasks and its impact on the labor market.

Research Background and Objectives

The rapid advancement of artificial intelligence (AI) has profound implications for the labor market. However, systematic empirical research on AI’s actual application in economic tasks remains scarce. This study introduces a novel framework that maps over four million conversations on Claude.ai to occupational categories from the U.S. Department of Labor’s O*NET database, identifying AI usage patterns and its impact on various professions. The research objectives include:

1. Measuring the scope of AI adoption in economic tasks, identifying which tasks and professions are most affected by AI.

2. Quantifying the depth of AI usage within occupations, assessing the extent of AI penetration in different job roles.

3. Evaluating AI’s application in different occupational skills, identifying the cognitive and technical skills where AI is most frequently utilized.

4. Analyzing the correlation between AI adoption, wage levels, and barriers to entry, determining whether AI usage aligns with occupational salaries and skill requirements.

5. Differentiating AI’s role in automation versus augmentation, assessing whether AI primarily functions as an automation tool or an augmentation assistant enhancing human productivity.

Key Research Findings

1. AI Usage is Predominantly Concentrated in Software Development and Writing Tasks

• The most frequently AI-assisted tasks include software engineering (e.g., software development, data science, IT services) and writing (e.g., technical writing, content editing, marketing copywriting), together accounting for nearly 50% of total AI usage.

• Approximately 36% of occupations incorporate AI for at least 25% of their tasks, indicating AI’s early-stage integration into diverse industry roles.

• Occupations requiring physical interaction (e.g., anesthesiologists, construction workers) exhibit minimal AI usage, suggesting that AI’s influence remains primarily within cognitive and text-processing domains.

2. Quantifying the Depth of AI Integration Within Occupations

• Only 4% of occupations utilize AI for over 75% of their tasks, indicating deep AI integration in select job roles.

• 36% of occupations leverage AI for at least 25% of tasks, signifying AI’s expanding role in various professional task portfolios, though full-scale adoption is still limited.

3. AI Excels in Tasks Requiring Cognitive Skills

• AI is most frequently employed for tasks that demand reading comprehension, writing, and critical thinking, while tasks requiring installation, equipment maintenance, negotiation, and management see lower AI usage.

• This pattern underscores AI’s suitability as a cognitive augmentation tool rather than a substitute for physically intensive or highly interpersonal tasks.

4. Correlation Between AI Usage, Wage Levels, and Barriers to Entry

• Wage Levels: AI adoption peaks in mid-to-high-income professions (upper quartile), such as software development and data analysis. However, very high-income (e.g., physicians) and low-income (e.g., restaurant workers) occupations exhibit lower AI usage, possibly due to:

  • High-income roles often requiring highly specialized expertise that AI cannot yet fully replace.

  • Low-income roles frequently involving significant physical tasks that are less suited for AI automation.

• Barriers to Entry: AI is most frequently used in occupations requiring a bachelor’s degree or higher (Job Zone 4), whereas occupations with the lowest (Job Zone 1) or highest (Job Zone 5) education requirements exhibit lower AI usage. This suggests that AI is particularly effective in knowledge-intensive, mid-tier skill professions.

5. AI’s Dual Role in Automation and Augmentation

• AI usage can be categorized into:

  • Automation (43%): AI directly executes tasks with minimal human intervention, such as document formatting, marketing copywriting, and code debugging.

  • Augmentation (57%): AI collaborates with users in refining outputs, optimizing code, and learning new concepts.

• The findings indicate that in most professions, AI is utilized for both automation (reducing human effort) and augmentation (enhancing productivity), reinforcing AI’s complementary role in the workforce.

Research Methodology

This study employs the Clio system (Tamkin et al., 2024) to classify and analyze Claude.ai’s vast conversation data, mapping it to O*NET’s occupational categories. The research follows these key steps:

1. Data Collection:

   • AI usage data from December 2024 to January 2025, encompassing one million interactions from both free and paid Claude.ai users.

   • Data was analyzed with strict privacy protection measures, excluding interactions from enterprise customers (API, team, or enterprise users).

2. Task Classification:

   • O*NET’s 20,000 occupational tasks serve as the foundation for mapping AI interactions.

   • A hierarchical classification model was applied to match AI interactions with occupational categories and specific tasks.

3. Skills Analysis:

   • The study mapped AI conversations to 35 occupational skills from O*NET.

   • Special attention was given to AI’s role in complex problem-solving, system analysis, technical design, and time management.

4. Automation vs. Augmentation Analysis:

   • AI interactions were classified into five collaboration modes:

     • Automation Modes: Directive execution, feedback-driven corrections.

     • Augmentation Modes: Task iteration, knowledge learning, validation.

   • Findings indicate a near 1:1 split between automation and augmentation, highlighting AI’s varied applications across different tasks.

Policy and Economic Implications

1. Comparing Predictions with Empirical Findings

• The research findings validate some prior AI impact predictions while challenging others:

  • Webb (2019) predicted AI’s most significant impact in high-income occupations; however, this study found that mid-to-high-income professions exhibit the highest AI adoption, while very high-income professions (e.g., doctors) remain less affected.

  • Eloundou et al. (2023) forecasted that 80% of occupations would see at least 10% of tasks impacted by AI. This study’s empirical data shows that approximately 57% of occupations currently use AI for at least 10% of their tasks, slightly below prior projections but aligned with expected trends.

2. AI’s Long-Term Impact on Occupations

• AI’s role in augmenting rather than replacing human work suggests that most occupations will evolve rather than disappear.

• Policy recommendations:

  • Monitor AI-driven workforce shifts to identify which occupations benefit and which face displacement risks.

  • Adapt education and workforce training programs to ensure workers develop AI collaboration skills rather than being displaced by automation.

Conclusion

This research systematically analyzes over four million Claude.ai conversations to assess AI’s integration into economic tasks, revealing:

• AI is primarily applied in software development, writing, and data analysis tasks.

• AI adoption is widespread but not universal, with 36% of occupations utilizing AI for at least 25% of tasks.

• AI usage exhibits a balanced distribution between automation (43%) and augmentation (57%).

• Mid-to-high-income occupations requiring a bachelor’s degree show the highest AI adoption, while low-income and elite specialized professions remain less affected.

As AI technologies continue to evolve, their role in the economy will keep expanding. Policymakers, businesses, and educators must proactively leverage AI’s benefits while mitigating risks, ensuring AI serves as an enabler of productivity and workforce transformation.

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HaxiTAG PreSale BOT: Build Your Conversions from Customer login

With the rapid advancement of digital technology, businesses face increasing challenges, especially in efficiently converting website visitors into actual customers. Traditional marketing and customer management approaches are becoming cumbersome and costly. To address this challenge, HaxiTAG PreSale BOT was created. This embedded intelligent solution is designed to optimize the conversion process of website visitors. By harnessing the power of LLM (Large Language Models) and Generative AI, HaxiTAG PreSale BOT provides businesses with a robust tool, making customer acquisition and conversion more efficient and precise.

                Image: From Tea Room to Intelligent Bot Reception

1. Challenges of Reaching Potential Customers

In traditional customer management, converting potential customers often involves high costs and complex processes. From initial contact to final conversion, this lengthy process requires significant human and resource investment. If mishandled, the churn rate of potential customers will significantly increase. As a result, businesses are compelled to seek smarter and more efficient solutions to tackle the challenges of customer conversion.

2. Automation and Intelligence Advantages of HaxiTAG PreSale BOT

HaxiTAG PreSale BOT simplifies the pre-sale service process by automatically creating tasks, scheduling professional bots, and incorporating human interaction. Whether during a customer's first visit to the website or during subsequent follow-ups and conversions, HaxiTAG PreSale BOT ensures smooth transitions throughout each stage, preventing customer churn due to delays or miscommunication.

This automated process not only reduces business operating costs but also greatly improves customer satisfaction and brand loyalty. Through in-depth analysis of customer behavior and needs, HaxiTAG PreSale BOT can adjust and optimize touchpoints in real-time, ensuring customers receive the most appropriate service at the most opportune time.

3. End-to-End Digital Transformation and Asset Management

The core value of HaxiTAG PreSale BOT lies in its comprehensive coverage and optimization of the customer journey. Through digitalized and intelligent management, businesses can convert their customer service processes into valuable assets at a low cost, achieving full digital transformation. This intelligent customer engagement approach not only shortens the time between initial contact and conversion but also reduces the risk of customer churn, ensuring that businesses maintain a competitive edge in the market.

4. Future Outlook: The Core Competitiveness of Intelligent Transformation

In the future, as technology continues to evolve and the market environment shifts, HaxiTAG PreSale BOT will become a key competitive edge in business marketing and service, thanks to its efficient conversion capabilities and deep customer insights. For businesses seeking to stay ahead in the digital wave, HaxiTAG PreSale BOT is not just a powerful tool for acquiring potential customers but also a vital instrument for achieving intelligent transformation.

By deeply analyzing customer profiles and building accurate conversion models, HaxiTAG PreSale BOT helps businesses deliver personalized services and experiences at every critical touchpoint in the customer journey, ultimately achieving higher conversion rates and customer loyalty. Whether improving brand image or increasing sales revenue, HaxiTAG PreSale BOT offers businesses an effective solution.

HaxiTAG PreSale BOT is not just an embedded intelligent tool; it features a consultative and service interface for customer access, while the enterprise side benefits from statistical analysis, customizable data, and trackable customer profiles. It represents a new concept in customer management and marketing. By integrating LLM and Generative AI technology into every stage of the customer journey, HaxiTAG PreSale BOT helps businesses optimize and enhance conversion rates from the moment customers log in, securing a competitive advantage in the fierce market landscape.

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Utilizing Generative AI and LLM Tools for Competitor Analysis: Gaining a Competitive Edge

In today’s fiercely competitive market, how businesses conduct in-depth competitor analysis to identify market opportunities, optimize strategies, and devise plans to outmaneuver competitors is crucial to maintaining a leading position. HaxiTAG, through its robust AI-driven market research tools, offers comprehensive solutions for competitor analysis, helping businesses stand out in the competition.

Core Features and Advantages of HaxiTAG Tools

1. Data Collection and Integration
   HaxiTAG tools utilize AI technology to automatically gather public information about competitors from multiple data sources, such as market trends, consumer feedback, financial data, and product releases. This data is integrated and standardized to ensure accuracy and consistency, laying a solid foundation for subsequent analysis.

2. Competitor Analysis
   Once the data is collected, HaxiTAG employs advanced AI algorithms to conduct in-depth analysis. The tools identify competitors’ strengths, weaknesses, market strategies, and potential risks, providing businesses with comprehensive and detailed insights into their competitors. The analysis results are presented in a visualized format, making it easier for businesses to understand and apply the findings.

3. Trend Forecasting and Opportunity Identification
   HaxiTAG tools not only focus on current market conditions but also use machine learning models to predict future market trends. Based on historical data and market dynamics, the tools help businesses identify potential market opportunities and adjust their strategies accordingly to gain a competitive edge.

4. Strategic Optimization Suggestions
   Based on AI analysis results, the tools offer specific action recommendations to help businesses optimize existing strategies or develop new ones. These suggestions are highly targeted and practical, enabling businesses to effectively respond to competitors’ challenges.

5. Continuous Monitoring and Adjustment
   Markets are dynamic, and HaxiTAG supports real-time monitoring of competitors’ activities. By promptly identifying new threats or opportunities, businesses can quickly adjust their strategies based on real-time data, ensuring they maintain flexibility and responsiveness in the market.

Beginner’s Guide to Practice

• Getting Started
  New users can input target markets and key competitors’ information into the HaxiTAG platform, which will automatically gather and present relevant data. This process simplifies traditional market research steps, allowing users to quickly enter the core aspects of competitor analysis.

• Understanding Analysis Results
  Users need to learn how to interpret AI-generated analysis reports and visual charts. Understanding this data and grasping competitors’ market strategies are crucial for formulating effective action plans.

• Formulating Action Plans
  Based on the optimization suggestions provided by HaxiTAG tools, users can devise specific action steps and continuously monitor their effectiveness during implementation. The tools’ automated recommendations ensure that strategies are highly targeted.

• Maintaining Flexibility
  Given the ever-changing market environment, users should regularly use HaxiTAG tools for market monitoring and timely strategy adjustments to maintain a competitive advantage.

Limitations and Constraints

• Data Dependency
  HaxiTAG’s analysis results depend on the quality and quantity of available data. If data sources are limited or inaccurate, it may affect the accuracy of the analysis. Therefore, businesses need to ensure the breadth and reliability of data sources.

• Market Dynamics Complexity
  Although HaxiTAG tools can provide detailed market analysis and forecasts, the dynamic and unpredictable nature of the market may exceed the predictive capabilities of AI models. Thus, final strategic decisions still require human expertise and judgment.

• Implementation Challenges
  For beginners, although HaxiTAG tools offer detailed strategic suggestions, effectively implementing these suggestions may still be challenging. This may require deeper market knowledge and execution capabilities.

Conclusion

By utilizing Generative AI and LLM technologies, HaxiTAG helps businesses gain critical market insights and strategic advantages in competitor analysis. The core strength lies in the automated data processing and in-depth analysis, providing businesses with precise, real-time market insights to maintain a leading position in the competitive landscape. Despite some challenges, HaxiTAG’s comprehensive advantages make it an indispensable tool for businesses in market research and competitor analysis.

By leveraging this tool, business partners can better seize market opportunities, devise action plans that surpass competitors, and ultimately achieve an unassailable position in the competition.

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SEO/SEM Application Scenarios Based on LLM and Generative AI: Leading a New Era in Digital Marketing

With the rapid development of Large Language Models (LLMs) and Generative Artificial Intelligence (Generative AI), the fields of SEO and SEM are undergoing revolutionary changes. By leveraging deep natural language understanding and generation capabilities, these technologies are demonstrating unprecedented potential in SEO/SEM practices. This article delves into the application scenarios of LLM and Generative AI in SEO/SEM, providing detailed scenario descriptions to help readers better understand their practical applications and the value they bring.

Core Values and Innovations

1. Intelligent SEO Evaluation Scenario
   Imagine a company's website undergoing regular SEO health checks. Traditional SEO analysis might require manual page-by-page checks or rely on tools that generate basic reports based on rigid rules. With LLM, the system can read the natural language content of web pages, understand their semantic structure, and automatically assess SEO-friendliness using customized prompts. Generative AI can then produce detailed and structured evaluation reports, highlighting keyword usage, content quality, page structure optimization opportunities, and specific improvement suggestions. For example, if a webpage has uneven keyword distribution, the system might suggest, "The frequency of the target keyword appearing in the first paragraph is too low. It is recommended to increase the keyword's presence in the opening content to improve search engine crawl efficiency." Such detailed advice helps SEO teams make effective adjustments in the shortest possible time.

2. Competitor Analysis and Differentiation Strategy
   When planning SEO strategies, companies often need to understand their competitors' strengths and weaknesses. With LLM and Generative AI, the system can quickly extract content from competitors' websites, perform semantic analysis, and compare it with the company's own content. Based on the analysis, the system generates a detailed report, highlighting the strengths and weaknesses of competitors in terms of keyword coverage, content depth, user experience, and offers targeted optimization suggestions. For instance, the system might find that a competitor has extensive high-quality content in the "green energy" sector, while the company's content in this area is relatively weak. The system would then recommend increasing the production of such content and suggest potential topics, such as "Future Trends in Green Energy" and "Latest Advances in Green Energy Technologies."

3. Personalized Content Generation
   In content marketing, efficiently producing high-quality content has always been a challenge. Through LLM's semantic understanding and Generative AI's generation capabilities, the system can automatically generate content that meets SEO requirements and has a high degree of originality based on the company's business themes and SEO best practices. This content not only improves search engine rankings but also precisely meets the needs of the target audience. For example, the system can automatically generate an article on "The Application of Artificial Intelligence in Healthcare" based on user-input keywords and target audience characteristics. This article would not only cover the latest industry developments but also, through in-depth content analysis, address the key pain points and needs of the target audience, significantly enhancing the article's appeal and utility.

4. User Profiling and Precision Marketing
   In digital marketing, understanding user behavior and devising precision marketing strategies are key to improving conversion rates. By analyzing vast amounts of user behavior data, LLM can build detailed user profiles and provide personalized SEO and SEM optimization suggestions based on these profiles. The system generates a detailed user analysis report based on users' search history, click behavior, and social media interactions, supporting the development of precise traffic acquisition strategies. For example, the system might identify that a particular user group is especially interested in "smart home" products and frequently searches for content related to "home automation" and "smart appliances." Based on this, the system would recommend that the company increase the production of such content and place related keywords in SEM ads to attract more users of this type.

5. Comprehensive Link Strategy Optimization
   Link strategy is an important component of SEO optimization. With LLM's unified semantic understanding model, the system can intelligently analyze the structure of internal and external links on a website and provide optimization suggestions. For instance, the system can analyze the distribution of internal links, identify whether there are unreasonable link structures between pages, and suggest improvements. The system also evaluates the quality and quantity of external links, recommending which external links need strengthening or adjustment. The system might point out, "A high-value content page has too few internal links, and it is recommended to increase the number of internal links to this page to enhance its weight." Additionally, the system might suggest strengthening cooperation with certain high-quality external websites to improve the overall SEO effectiveness of the site.

6. Automated SEM Strategy Design
   In SEM ad placement, selecting the right keywords and devising effective placement strategies are crucial. By analyzing market keyword trends, competition levels, and user intent, the system can automatically generate SEM placement strategies. The generated strategies will include suggested keyword lists, budget allocation, ad copy suggestions, and regular real-time data analysis reports to help companies continuously optimize ad performance. For example, the system might discover that "certain long-tail keywords have lower competition but higher potential conversion rates, and it is recommended to increase the placement of these keywords." The system would also track the performance of the ads in real-time, providing adjustment suggestions, such as "reduce budget allocation for certain low-conversion keywords to improve overall ROI."

Practical Application Scenarios and Functional Value

1. SEO-Friendliness Evaluation: By fine-tuning prompts, the system can perform SEO evaluations for different types of pages (e.g., blog posts, product pages) and generate detailed reports to help companies identify areas for improvement.

2. Competitor Website Analysis: The system can evaluate not only the company's website but also analyze major competitors' websites and generate comparison reports to help the company formulate differentiated SEO strategies.

3. Content Optimization Suggestions: Based on SEO best practices, the system can provide suggestions for keyword optimization, content layout adjustments, and more to ensure content is not only search engine friendly but also improves user experience.

4. Batch Content Generation: The system can handle large volumes of content needs, automatically generating SEO-friendly articles while ensuring content coherence and relevance, thus improving content production efficiency.

5. Data Tracking and Optimization Strategies: The system can track a website's SEO and SEM data in real time and provide optimization suggestions based on data changes, helping companies maintain a competitive edge.

6. User Behavior Analysis and Traffic Strategy: Through detailed user profiling, the system can help companies better understand user needs and adjust SEO and SEM strategies accordingly to improve conversion rates.

7. Link Strategy Optimization: The system can assist in optimizing internal links and, by analyzing external link data, provide suggestions for building external links to enhance the overall SEO effectiveness of the website.

8. SEM Placement Optimization: Through real-time market analysis and ad performance tracking, the system can continuously optimize SEM strategies, helping companies maximize the effectiveness of their ad placements.

Conclusion

The SEO/SEM application scenarios based on LLM and Generative AI provide companies with new optimization pathways. From evaluation to content generation, user analysis, and link strategy optimization, LLM and Generative AI are reshaping SEO and SEM practices. As these technologies mature, companies will encounter more innovation and opportunities in digital marketing, achieving more efficient and precise marketing results.

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Unlocking the Future of Customer Interaction and Market Research: The Transformative Power of HaxiTAG AI for Comprehensive Coverage and Precise Insights

HaxiTAG AI is introducing this groundbreaking new technology into market research, customer support, and customer-facing service interactions. Whether it’s customer support, sales, or customer success teams, every conversation with your customers is an opportunity to understand your business and identify customer needs.

Understanding Customer and Market Challenges

1. Issues to Explore and Analyze:
   The problems that need to be examined in-depth.

2. Questions Needing Immediate Research:
   Inquiries from customers that require prompt investigation.

3. Signals from Daily Operations:
   Routine activities that may reveal underlying issues. While most companies have a general grasp of categories they need to manage, there's often a wealth of untapped information due to human resource limitations.

4. Listening to Customers:
   Strive to listen to your customers as thoroughly as possible and understand them within your capacity. However, as your company grows and the number of customers increases, daily communication with them may become challenging.

The Scale Problem in Customer and Market Interactions

This issue indeed accompanies success. When the number of customers is manageable, you can typically leverage your staff, sales teams, or customer support teams to gain insights and better guide your company toward greater revenue growth. But as you expand to a size where managing these vast conversations becomes nearly impossible, you’ll realize that much is happening without your awareness.

Traditional Methods of Customer Data Analysis

We believe that every large-scale enterprise is attempting to manually review and conduct small-sample analyses, aiming to collect and evaluate about 5% of conversations. This may involve checking compliance matters, like how agents handle situations, or identifying common themes in these conversations.

Ultimately, this is just sampling, and everyone is dissatisfied because they understand that it’s not a very accurate process. Then you begin involving engineers to write scripts, perform post-analysis, extract data from various customer interaction systems, and conduct lengthy analyses. Eventually, you hope to gain insights that can be tracked in the future.

The Role of Generative AI in Transformation

Next, you enter a stage of building software to look for very specific content in every conversation. But everything is retrospective—events have already occurred, and you were unaware of the signs. This is where generative AI can truly change the process.

Generative AI unlocks the incredible ability to cover 100% of the data. Now, you can use generative AI to discover things you didn’t even know you were looking for, reviewing everything at once, rather than just sampling or seeking known issues.

Practical Examples of AI in Customer Interactions

Here’s a great example: a brief interaction with a random agent handling customer chat. From this customer message, you can identify the reason for the customer’s communication—that’s your intent. Which aspects of our business are truly the root cause of this issue? The router, damaged delivery—perhaps it’s a supply chain issue. You can also gauge emotions, not just of the customer but also of your agent, which may be even more critical.

In the end, through every message, you can extract more in-depth information from a conversation than ever before. This is the service our platform strives to provide.

The Actual Impact of the HaxiTAG AI Platform

Here’s a great example from one of our clients, a wind power operator. One insight we provided was identifying defects in their wind turbine operations and maintenance. Some issues might persist for weeks without IT technical support to uncover them, potentially evolving into bigger problems. But our platform can detect these issues in real-time, significantly increasing the power generation revenue from their operations and maintenance.

The Process Behind AI Technology

How does all this work? It all starts with collecting all these conversations. This part is the non-AI mundane work, where we connect to numerous contact systems, ticket systems, and so forth. We pull all this information in, normalize it, clean it thoroughly, and prepare it for compression and processing by LLM prompts.

We have dozens of pipelines to evaluate these conversations in different ways, all of which can be configured by the user. Our customers can tell us what they care about, what they are searching for, and they actually collaborate with us to craft these prompts. Ultimately, they write the prompts themselves and manage them over time.

The Critical Importance of Accuracy in Enterprise AI

Why is accuracy ultimately the most important? When dealing with enterprise-scale operations, the primary concern is accuracy. There’s significant market concern about accuracy. Can I deploy generative AI to try to understand these conversations and truly trust these insights? When we work with customers, within seven days, we aim to demonstrate these insights to them. From that point forward, we strive to achieve 97% accuracy. However, this requires extensive sampling and trial and error. Ultimately, we seek to build trust with our customers because that will ensure they continue to renew and become long-term clients.

The Role of HaxiTAG AI in AI Implementation

HaxiTAG AI plays a crucial role in helping us achieve this goal. They not only provide our engineering team with a plethora of features and capabilities but also assist wind power domain experts, not IT specialists, in understanding the quality of the code they write through standardized components and interactive experiences. More importantly, our solution engineers and implementation engineers work with customers to debug and ultimately receive positive feedback. Customers tell us, “For certain things, the HaxiTAG AI tool is the go-to tool in this process.”

Conclusion and the Future of Self-Improving AI Systems

HaxiTAG AI has built an infrastructure layer in generative AI programs and LLM-driven large-scale data and knowledge application solutions to enhance the accuracy and reliability of AI applications while significantly lowering the barrier to entry. Our initial vision was to build a self-improving system—a system with LLM applications capable of refining prompts and models, ultimately driving accuracy and enhancing the utility of customer digital transformation.

The vision we are striving to achieve is one where HaxiTAG AI helps you turn your business data into assets, build new competitive advantages, and achieve better growth.

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How to Deeply Understand Your Users and Customers: Online Marketing and Target Market Reach

In today’s competitive market environment, understanding your users and customers is crucial for successful marketing. This not only includes knowing who they are but also identifying where they are and how to effectively reach and convert them. Below are some strategies for deeply analyzing users and customers, and how to reach the target market through online marketing.

1. Understanding User Paths and Behavior
   First, it’s vital to understand how users find your brand or product. What search queries did they use? Through which sources did they land on your page? What links did they click on? Answering these questions can help you optimize user experience and improve conversion rates. By using data analysis tools like Google Analytics, you can record and analyze this data to build strong insights. These insights allow businesses to turn data into valuable knowledge, supporting more in-depth market analysis and research.

2. Analyzing Users' Associated Interests
   It’s important not only to understand what users visit on your site but also what other information they seek. This information often requires professional service providers to collect and analyze. By analyzing associated interests, businesses can better understand customers' needs and preferences, further segment the market, and develop more targeted marketing strategies.

3. Researching Competitors' User Profiles
   Understanding the user profiles of competitors is equally important. This involves not only identifying who their customers are but also understanding what other information these customers seek. To acquire such cross-platform and cross-media data, companies usually rely on professional service providers. These providers can integrate relevant data, offering deep market insights to support business decisions and operations.
   

HaxiTAG’s Data intelligence Solutions

HaxiTAG offers comprehensive data collection, analysis, and application solutions, helping companies integrate upstream and downstream data partners. This provides technical support for marketing, communication, customer identification, and growth. These services provide robust support for business development, helping companies stand out in the competition.

Understanding users and customers is the foundation of successful marketing. By analyzing user paths, behaviors, and competitor data, companies can create more precise and effective marketing strategies. HaxiTAG’s solutions provide strong data support, helping companies better identify and convert potential customers, ultimately establishing long-term partnerships. In today’s business environment, this data-driven insight is a key driver of enterprise growth. 

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In-Depth Exploration of SEO Keyword Data Analysis: How to Use Tools to Enhance Content Strategy

In the world of digital marketing, SEO (Search Engine Optimization) is undoubtedly crucial for any business aiming to establish itself online. However, with the development of the internet, SEO has evolved beyond simple keyword placement into a comprehensive strategy involving data analysis, competitor research, and trend identification. This article delves into how advanced tools like ChatGPT, Claude, Ahrefs, Similarweb, and Semrush can be used to analyze SEO keyword data and extract powerful SEO insights to build a solid foundation for content strategy.

Multi-Tool Integration: Building Comprehensive Keyword Insights

In SEO keyword research, relying on a single tool often fails to provide a sufficiently comprehensive perspective. By integrating data from Ahrefs, Semrush, Similarweb, and other tools, we can gain deeper insights into keywords from various angles. Ahrefs and Semrush excel in providing data on keyword search volume, difficulty, and competitor usage, while Similarweb can analyze competitors' traffic sources to help identify market gaps. Additionally, by leveraging the semantic analysis capabilities of ChatGPT or Claude, we can uncover potential long-tail keywords and user intents, offering more precise guidance for content creation.

This multi-tool approach not only broadens the scope of keyword coverage but also enhances data accuracy through cross-validation, reducing the risk of errors associated with relying on a single data source.

In-Depth Competitor Analysis: Discovering Opportunities from Competition

SEO competition is increasingly fierce, and finding one's breakthrough point in the market is a common challenge. Tools like Ahrefs and Similarweb play a key role here. By inputting competitors' domains into Ahrefs, we can analyze their keyword rankings, traffic pages, and uncover their SEO strategy's strengths and weaknesses. Similarweb further provides insights into competitors' traffic composition, helping us identify untapped market opportunities.

Such in-depth competitor analysis not only helps in positioning ourselves in the market but also allows us to refine our SEO practices by learning from competitors' strategies and avoiding their mistakes.

Capturing Trends: Identifying Emerging Keywords and Market Opportunities

Grasping trends is crucial for developing successful SEO strategies. Tools like Semrush and Ahrefs offer trend analysis features that help identify emerging keywords and underutilized SEO opportunities in the market. These emerging trends often indicate future traffic growth points, allowing us to gain a competitive edge by targeting these keywords before our competitors.

However, SEO strategies are not static. The rapid pace of market changes requires ongoing tracking of keyword performance and flexible adjustments to content strategies. Regular use of these tools to monitor trend data and adjust strategies accordingly ensures that our content remains competitive.

Practical Guide: How to Effectively Utilize These Tools

For newcomers to SEO, effectively utilizing these tools is essential. Firstly, selecting the appropriate combination of tools is critical. Starting with Ahrefs and Semrush to learn how to interpret keyword data, and then expanding to Similarweb and ChatGPT, is a good approach. Secondly, keyword analysis should be gradual, beginning with a narrow range of keywords and progressively expanding to broader areas.

When formulating content strategies, particularly focus on trend data to ensure content has long-term traffic potential. Lastly, SEO is an ongoing process of learning and adjustment. Regularly revisiting strategies ensures that content remains adaptable to market changes.

Limitations and Challenges

Although combining multiple tools for SEO analysis offers many advantages, there are inherent limitations. First, data discrepancies between different tools can occur, requiring cross-validation with multiple data sources to improve accuracy. Second, SEO strategies must be continually adjusted, and static keyword analysis may not address rapidly changing market demands. Third, there may be a learning curve for new users in mastering these tools, which could be time-consuming. Lastly, the subscription costs for advanced SEO tools like Ahrefs and Semrush can be high, potentially straining the budgets of small and medium-sized enterprises.

In summary, SEO keyword data analysis is a complex yet highly valuable task. By effectively integrating tools like ChatGPT, Claude, Ahrefs, Similarweb, and Semrush, businesses can more precisely select keywords, develop forward-looking content strategies, and continuously optimize SEO performance. However, success depends on ongoing monitoring and flexible adjustment of strategies to respond to the evolving market environment and competitors' dynamics. This requires not only deep expertise but also continuous practical experience to maintain a competitive edge in the SEO landscape.

As an expert in GenAI-driven intelligent industry application, HaxiTAG studio is helping businesses redefine the value of knowledge assets. By deeply integrating cutting-edge AI technology with business applications, HaxiTAG not only enhances organizational productivity but also stands out in the competitive market. As more companies recognize the strategic importance of intelligent knowledge management, HaxiTAG is becoming a key force in driving innovation in this field. In the knowledge economy era, HaxiTAG, with its advanced EiKM system, is creating an intelligent, digital knowledge management ecosystem, helping organizations seize opportunities and achieve sustained growth amidst digital transformation.

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The New Era of AI-Driven Innovation

In today's rapidly evolving business landscape, Artificial Intelligence (AI) is profoundly transforming our work methods and innovation processes. As an expert in AI products and innovation, I am thrilled to introduce some cutting-edge AI-assisted tools and explore how they play crucial roles in innovation and decision-making. This article will delve into AI products such as ChatGPT, Claude, Poe, Perplexity, and Gemini, showcasing how they drive innovation and foster human-machine collaboration.

ChatGPT: A Powerful Ally in Creative Generation and Text Analysis

Developed by OpenAI, ChatGPT has gained renown for its exceptional natural language processing capabilities. It excels in creative generation, text analysis, and coding assistance, swiftly producing diverse ideas, aiding in copywriting, and solving programming challenges. Whether for brainstorming or executing specific tasks, ChatGPT provides invaluable support.

Claude: The Expert in Deep Analysis and Strategic Planning

Claude, created by Anthropic, stands out with its superior contextual understanding and reasoning abilities. It particularly shines in handling complex tasks and extended dialogues, making significant contributions in deep analysis, strategic planning, and academic research. For innovation projects requiring profound insights and comprehensive thinking, Claude offers forward-looking and strategic advice.

Poe: A Platform Integrating Multiple Models

As a platform integrating various AI models, Poe offers users the flexibility to choose different models. This diversity makes Poe an ideal tool for tackling various tasks and comparing the effectiveness of different models. In the innovation process, Poe allows teams to leverage the unique strengths of different models, providing multi-faceted solutions to complex problems.

Perplexity: The New Trend Combining AI with Search Engines

Perplexity represents the emerging trend of combining AI with search engines. It provides real-time, traceable information, particularly suitable for market research, competitive analysis, and trend insights. In the fast-paced innovation environment, Perplexity can swiftly gather the latest market dynamics and industry information, offering timely and reliable data support for decision-makers.

Gemini: The Pioneer of Multimodal AI Models

Google's latest multimodal AI model, Gemini, demonstrates exceptional ability in processing various data types, including text and images. It excels in complex scenario analysis and multimedia content creation, capable of handling challenging tasks such as visual creative generation and cross-media problem analysis. Gemini's multimodal features bring new possibilities to the innovation process, making cross-disciplinary innovation more accessible.

Building a Robust Innovation Ecosystem

These AI tools collectively construct a powerful innovation ecosystem. By integrating their strengths, organizations can comprehensively enhance their innovation capabilities, improve decision quality, accelerate innovation cycles, explore new innovation frontiers, and optimize resource allocation. A typical AI-assisted innovation process might include the following steps:

1. Problem Definition: Human experts clearly define innovation goals and constraints.
2. AI-Assisted Research: Utilize tools like Perplexity for market research and data analysis.
3. Idea Generation: Use ChatGPT or Claude to generate initial innovative solutions.
4. Human Evaluation: Expert teams assess AI-generated proposals and provide feedback.
5. Iterative Optimization: Based on feedback, use tools like Gemini for multi-dimensional optimization.

Wise AI Product Selection Strategy

To maximize the benefits of AI tools, organizations need to formulate a prudent AI product selection strategy:

• Choose the most suitable AI tools based on task complexity and characteristics.
• Fully leverage the advantages of different AI tools to optimize the decision-making process.
• Encourage human experts to become proficient users and coordinators of AI tools.

Through this approach, organizations can maintain the core position of human creativity and judgment while fully harnessing the advantages of AI technology, achieving a more efficient and effective innovation process.

The Future Path of Innovation

AI technology is rapidly evolving, with new tools and models constantly emerging. Therefore, staying abreast of the latest developments in the AI field and flexibly adjusting application strategies is crucial for maintaining innovation advantages. AI products like ChatGPT, Claude, Poe, Perplexity, and Gemini are reshaping innovation processes and decision-making methods. They are not just powerful auxiliary tools but keys to unlocking new thinking and possibilities. By wisely integrating these AI tools, organizations can build a more efficient, flexible, and innovative work environment, maintaining a leading position in the competitive market. Future success will belong to those organizations that can skillfully balance human wisdom with AI capabilities.

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Unlocking the Power of Human-AI Collaboration: A New Paradigm for Efficiency and Growth

As artificial intelligence (AI) technology continues to advance at an unprecedented rate, particularly with the emergence of large language models (LLMs) and generative AI (GenAI) products, we are witnessing a profound transformation in the way we work and live. This article delves into how LLMs and GenAI products are revolutionizing human-AI collaboration, driving efficiency and growth at individual, organizational, and societal levels.

The New Paradigm of Human-AI Collaboration

LLMs and GenAI products are pioneering a new model of human-AI collaboration that goes beyond simple task automation, venturing into complex cognitive domains such as creative generation, decision support, and problem-solving. AI assistants like ChatGPT, Claude, and Gemini are becoming our intelligent partners, providing insights, suggestions, and solutions at our fingertips.

Personal Efficiency Revolution

At the individual level, these AI tools are transforming how we work:

• Intelligent Task Management: AI can automate routine tasks, such as email categorization and scheduling, freeing us to focus on creative work.
• Knowledge Acceleration: AI systems like Perplexity can rapidly provide us with the latest and most relevant information, significantly reducing research and learning time.
• Creative Boosters: When we encounter creative roadblocks, AI can offer multi-dimensional inspiration and suggestions, helping us overcome mental barriers.
• Decision Support Tools: AI can quickly analyze vast amounts of data, providing objective suggestions and enhancing our decision-making quality.

Organizational Efficiency and Competitiveness

For organizations, the application of LLMs and GenAI products means:

• Cost Optimization: AI's automation of basic tasks can significantly reduce labor costs and improve operational efficiency.
• Innovation Acceleration: AI can facilitate market research, product development, and creative generation, enabling companies to quickly launch innovative products and services.
• Decision Optimization: AI's real-time data analysis capabilities can help companies make faster and more accurate market responses, enhancing competitiveness.
• Talent Empowerment: AI tools can serve as digital assistants, boosting each employee's work efficiency and creativity.

Societal Efficiency and Growth

From a broader perspective, the widespread adoption of LLMs and GenAI products is poised to significantly improve societal efficiency:

• Public Service Optimization: AI can help optimize resource allocation, improving service quality in government, healthcare, and other sectors.
• Educational Innovation: AI can provide personalized learning experiences for each student, enhancing education quality and efficiency.
• Scientific Breakthroughs: AI can assist in data analysis, model building, and accelerating scientific discovery.
• Social Problem-Solving: AI can offer more efficient analysis and solutions to global challenges, such as climate change and disease prevention.

Balancing Value and Risk

While LLMs and GenAI products bring immense value and efficiency gains, we must also acknowledge the associated risks:

• Technical Risks: AI systems may contain biases, errors, or security vulnerabilities, requiring continuous monitoring and improvement.
• Privacy Risks: Large-scale AI usage implies more data collection and processing, making personal data protection a critical issue.
• Ethical Risks: AI applications may raise ethical concerns, such as job displacement due to automation.
• Dependence Risks: Over-reliance on AI may lead to the degradation of human skills, necessitating vigilance.

Future Outlook

Looking ahead, LLMs and GenAI products will continue to deepen human-AI collaboration, reshaping our work and life. The key lies in establishing a balanced framework that harnesses AI's advantages while preserving human creativity and judgment. We must:

• Continuously Learn: Update our skills to collaborate effectively with AI.
• Think Critically: Cultivate critical thinking skills to evaluate AI outputs, rather than blindly relying on them.
• Establish an Ethical Framework: Develop a robust AI application ethics framework to ensure that technology development aligns with human values.
• Redesign Workflows: Optimize work processes to maximize human-AI collaboration.

LLMs and GenAI products are ushering in a new era of efficiency revolution. By wisely applying these technologies, we can achieve unprecedented success in personal growth, organizational development, and societal progress. The key is to maintain an open, cautious, and innovative attitude, embracing the benefits of technology while proactively addressing the challenges. Let us embark on this AI-driven new era, creating a more efficient, intelligent, and collaborative future together.

Join the HaxiTAG Community for Exclusive Insights

We invite you to become a part of the HaxiTAG community, where you'll gain access to a wealth of valuable resources. As a member, you'll enjoy:

1. Exclusive Reports: Stay ahead of the curve with our latest findings and industry analyses.
2. Cutting-Edge Research Data: Dive deep into the numbers that drive innovation in AI and technology.
3. Compelling Case Studies: Learn from real-world applications and success stories in various sectors.

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By joining our community, you'll be at the forefront of AI and technology advancements, with regular updates on our ongoing research, emerging trends, and practical applications. Don't miss this opportunity to connect with like-minded professionals and enhance your knowledge in this rapidly evolving field.

Join HaxiTAG today and be part of the conversation shaping the future of AI and technology!

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Challenges and Opportunities of Generative AI in Handling Unstructured Data

Building Data Architecture to Support Generative AI in Processing Both Structured and Unstructured Data

The ability of generative AI to handle unstructured data presents a significant challenge in the current field of artificial intelligence. Traditional data organization methods are primarily designed for structured data, whereas unstructured data, such as chat records, videos, and code, require more flexible and intelligent processing methods. As data types diversify, enterprises must reassess their data architectures to support the smooth implementation of generative AI initiatives.

Data Governance Strategy

Data governance is crucial for ensuring data quality and consistency. Enterprises should prioritize establishing a clear data governance strategy, equipping appropriate personnel, tools, and execution authority to transform data quality challenges into competitive advantages. Forming dedicated task forces or equivalent bodies to study the applications of generative AI and large language models (LLMs) can provide significant competitive benefits.

Data Storage Strategy

Data storage strategy is central to solving data management challenges. Research indicates that over half of stored data is inactive, meaning it is rarely or never accessed. Despite this, enterprises do not want to discard it because of its potential future value. Enterprises should reassess their existing storage capabilities and build modern automated storage architectures that allow easy access and processing of data throughout its lifecycle, thus enhancing data utilization.

Data Quality Strategy

Ensuring data quality is fundamental to the success of generative AI. Enterprises should make high data quality a strategic priority, appoint a Chief Data Officer, and allocate dedicated budgets and resources. Only high-quality data can effectively support AI models and help achieve business objectives.

Measuring Progress

Enterprise leadership should establish clear data assessment standards and success metrics. By regularly evaluating data quality and governance progress, enterprises can timely adjust their strategies to ensure the smooth advancement of generative AI initiatives.

Handling Unstructured Data

Generative AI models have higher requirements for data quality, especially unstructured data. In the next five years, unstructured data is expected to grow at a compound annual growth rate of 25%, making up 90% of new data created. This type of data includes high-resolution videos, complex medical data, genome sequencing, etc. Enterprises need to deploy automated data lifecycle management solutions and utilize AI technologies to extract higher business value.

Supporting Broad Use Cases with Data Architecture

Enterprises should build relevant functions into their existing data architectures, such as vector databases and data preprocessing pipelines, particularly for handling unstructured data. Integrating these functions can significantly enhance data processing efficiency and the broad applicability of AI solutions.

Using AI to Build AI

Generative AI can be used not only for data management but also to accelerate tasks across the data value chain, from data engineering to data governance and analysis. With the help of AI technologies, enterprises can optimize data processing workflows and improve overall data value chain efficiency.

Conclusion

The challenges of generative AI in handling unstructured data require enterprises to reassess their data governance and storage strategies and build modern data architectures. Through efficient data management and quality control, enterprises can fully leverage the potential of generative AI, gaining significant competitive advantages. In this rapidly evolving era, data quality and management capabilities will determine the success and future of enterprises.

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