Let\u2019s get started!<\/p>\n\n\n\n
<\/span>What is Chat2Query?<\/span><\/h2>\n\n\n\nForget the jargon. Forget the complicated queries. Chat2Query lets you ask questions in plain language and get answers straight from your data\u2014instantly.<\/p>\n\n\n\n
\n- Want to know, \u201cWhat were our sales last quarter?\u201d<\/li>\n\n\n\n
- Curious about, \u201cWhich product category performed the best?\u201d<\/li>\n\n\n\n
- Or maybe you\u2019re wondering, \u201cWhat are the trends in customer complaints this month?\u201d<\/li>\n<\/ul>\n\n\n\n
Just type your question in simple language, and Chat2Query will translate it into SQL, pull the data, and serve it up in an easy-to-read format, complete with visuals. It\u2019s as if your data speaks directly to you\u2014no SQL required.<\/p>\n\n\n\n
<\/span>How Does Chat2Query Work?<\/span><\/h2>\n\n\n\nIt\u2019s surprisingly simple\u2014and powerful.<\/p>\n\n\n\n
1. Enrich the Data Context<\/strong><\/h3>\n\n\n\nFirst, Chat2Query needs to get familiar with your data. To do this, it analyzes your database using both relational and vector databases. This hybrid approach allows Chat2Query to understand the structure of the database and the relationships between tables, columns, and entities. The vector database is key for enriching the context by storing more complex, high-dimensional data that helps Chat2Query better understand the relationships between data points. The more Chat2Query knows about the data, the more accurate and insightful the responses.<\/p>\n\n\n\n
2. Ask Your Questions<\/strong><\/h3>\n\n\n\nOnce the data context is enriched, you can start asking questions. Chat2Query then converts your question into a SQL query, pulls the data, and gives you your answer\u2014usually with a handy chart or graph to make it even easier to digest.<\/p>\n\n\n
\n![\"How](\"https:\/\/static.pingcap.com\/files\/2025\/01\/14144812\/29049897-903e-423d-935c-bb4af64ed753.png\")
<\/figure>\n<\/div>\n\n\nFigure 1. How Chat2Query works<\/em>.<\/p>\n\n\n\n<\/span>Why Chat2Query Works So Well<\/span><\/h2>\n\n\n\nFor Chat2Query to function effectively, it needs to understand the database schema. That\u2019s where \u201cUnderstand db\u201d comes in. It\u2019s like giving Chat2Query a roadmap of your data\u2014helping it understand relationships between tables, columns, and entities.<\/p>\n\n\n\n
\n- Why it matters:<\/strong> This step alone can improve SQL accuracy by 2-3% on benchmarks like Spider<\/a>. A small increase, but a big deal when you\u2019re dealing with large datasets.<\/li>\n<\/ul>\n\n\n\n
![\"\"](\"https:\/\/static.pingcap.com\/files\/2025\/01\/14144811\/331c44b7-d82d-4864-adff-3975ef7359da.png\")
<\/figure>\n<\/div>\n\n\nFigure 2. Understand db<\/em>.<\/p>\n\n\n\nPerfecting Prompts: Prompt Engineering<\/h3>\n\n\n\n
You can\u2019t just throw any question at a system and expect it to work perfectly. So, we engineer our prompts to ensure Chat2Query really nails it. By using advanced techniques like Chain of Thought (COT) and Retrieval Augmented Generation (RAG), we guide the system to reason through the question step-by-step, ensuring the most accurate SQL possible.<\/p>\n\n\n\n
\n- Why it matters:<\/strong> Our COT + RAG combo helps Chat2Query consistently crush benchmarks like Spider and BIRD. It\u2019s the secret sauce behind those impressive scores.<\/li>\n<\/ul>\n\n\n\n
![\"The](\"https:\/\/static.pingcap.com\/files\/2025\/01\/14144812\/8ec897cc-6422-4caf-bb64-c9ebbf3ed1d1.png\")
<\/figure>\n<\/div>\n\n\nFigure 3. The primary steps involved in prompt engineering<\/em>.<\/p>\n\n\n\nFine-Tuning with Post-Processing<\/h3>\n\n\n\n
Even with advanced technologies like LLMs, sometimes hallucinations can occur. That\u2019s why we use a multi-agent collaboration mechanism during post-processing. This system works like a team of experts reviewing the raw SQL output, identifying any inconsistencies, and refining the query to improve its accuracy.<\/p>\n\n\n\n
The multi-agent collaboration mechanism helps ensure that even if a model misses something, other components of the system step in to catch those issues and make necessary adjustments. This added layer of refinement significantly enhances the reliability of the SQL queries produced by Chat2Query.<\/p>\n\n\n\n
\n- Why it matters:<\/strong> This post-processing mechanism, along with the multi-agent system, increases the overall accuracy of SQL queries by 2-4%. It helps reduce errors and ensure that the output is stable, consistent, and ready for actionable insights.<\/li>\n<\/ul>\n\n\n\n
![\"\"](\"https:\/\/static.pingcap.com\/files\/2025\/01\/14144811\/a8e6e685-a939-48e8-a60a-e4001583985d.png\")
<\/figure>\n<\/div>\n\n\nFigure 4. Post-processing in action<\/em>.<\/p>\n\n\n\n<\/span>Real-World Use Cases: How Chat2Query Powers Smarter Decisions<\/span><\/h2>\n\n\n\nNow, let\u2019s talk about how Chat2Query is making a real difference for businesses:<\/p>\n\n\n\n
\n- Sales Performance:<\/strong> Ask \u201cHow much did our sales increase this month compared to last month?\u201d and instantly get the data you need to adjust your sales strategy. No more waiting on reports or pulling data manually.<\/li>\n\n\n\n
- Customer Insights:<\/strong> Quickly discover what your customers are saying. Ask \u201cWhat were the most common customer complaints this month?\u201d to identify areas that need attention and improve your service.<\/li>\n\n\n\n
- Supply Chain Optimization: <\/strong>Stay on top of your inventory by asking \u201cWhich products are below the safety stock level?\u201d or \u201cWhy did we experience delays last week?\u201d to adjust your supply chain strategy on the fly.<\/li>\n\n\n\n
- Financial Reporting:<\/strong> Need to know what caused fluctuations in the market? Simply ask, \u201cWhat were the main factors driving market changes this week?\u201d <\/strong>and get detailed insights to stay ahead of the competition.<\/li>\n<\/ul>\n\n\n\n
<\/span>Looking Ahead: The Future of Chat2Query<\/span><\/h2>\n\n\n\nChat2Query is still evolving, and we\u2019re continuously working to improve it. We\u2019re exploring new techniques and optimizations to make Text2SQL even more accurate and powerful. Our goal is to keep simplifying the way businesses interact with their data, making complex analysis accessible to anyone, regardless of their technical background.<\/p>\n\n\n\n
<\/span>Get Started with Chat2Query Today<\/span><\/h2>\n\n\n\nReady to see the magic of Chat2Query for yourself? It\u2019s time to make your data work harder for you.<\/p>\n\n\n\n
\n- Try TiDB Cloud for Free:<\/strong> Experience the power of Chat2Query with TiDB Cloud<\/a> and see how simple data analysis can be.<\/li>\n\n\n\n
- Schedule a Demo<\/strong>: Want to learn more? Book a session<\/a> with one of our experts to see how Chat2Query can transform your business.<\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"
What if you could interact with your data just like you would with a colleague? No need for complicated SQL queries or advanced data analysis tools\u2014just ask your data a question and get an immediate, clear answer. Sounds like magic, right? Well, it\u2019s not. It\u2019s TiDB’s Chat2Query, and it\u2019s changing the game in how businesses […]<\/p>\n","protected":false},"author":286,"featured_media":24871,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"ub_ctt_via":"","footnotes":""},"categories":[13],"tags":[356,357,360,359,298,111],"class_list":["post-24862","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-product","tag-chat2query","tag-data-analysis","tag-llms","tag-prompt-engineering","tag-rag","tag-tidb"],"acf":[],"featured_image_src":"https:\/\/static.pingcap.com\/files\/2025\/01\/14143944\/tidb_feature_1800x600-1-2.png","author_info":{"display_name":"Jway Wan","author_link":"https:\/\/www.pingcap.com\/ko\/blog\/author\/jwan\/"},"yoast_head":"\n
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Just type your question in simple language, and Chat2Query will translate it into SQL, pull the data, and serve it up in an easy-to-read format, complete with visuals. It\u2019s as if your data speaks directly to you\u2014no SQL required.<\/p>\n\n\n\n
<\/span>How Does Chat2Query Work?<\/span><\/h2>\n\n\n\nIt\u2019s surprisingly simple\u2014and powerful.<\/p>\n\n\n\n
1. Enrich the Data Context<\/strong><\/h3>\n\n\n\nFirst, Chat2Query needs to get familiar with your data. To do this, it analyzes your database using both relational and vector databases. This hybrid approach allows Chat2Query to understand the structure of the database and the relationships between tables, columns, and entities. The vector database is key for enriching the context by storing more complex, high-dimensional data that helps Chat2Query better understand the relationships between data points. The more Chat2Query knows about the data, the more accurate and insightful the responses.<\/p>\n\n\n\n
2. Ask Your Questions<\/strong><\/h3>\n\n\n\nOnce the data context is enriched, you can start asking questions. Chat2Query then converts your question into a SQL query, pulls the data, and gives you your answer\u2014usually with a handy chart or graph to make it even easier to digest.<\/p>\n\n\n
\n<\/figure>\n<\/div>\n\n\nFigure 1. How Chat2Query works<\/em>.<\/p>\n\n\n\n<\/span>Why Chat2Query Works So Well<\/span><\/h2>\n\n\n\nFor Chat2Query to function effectively, it needs to understand the database schema. That\u2019s where \u201cUnderstand db\u201d comes in. It\u2019s like giving Chat2Query a roadmap of your data\u2014helping it understand relationships between tables, columns, and entities.<\/p>\n\n\n\n
\n- Why it matters:<\/strong> This step alone can improve SQL accuracy by 2-3% on benchmarks like Spider<\/a>. A small increase, but a big deal when you\u2019re dealing with large datasets.<\/li>\n<\/ul>\n\n\n\n
<\/figure>\n<\/div>\n\n\nFigure 2. Understand db<\/em>.<\/p>\n\n\n\nPerfecting Prompts: Prompt Engineering<\/h3>\n\n\n\n
You can\u2019t just throw any question at a system and expect it to work perfectly. So, we engineer our prompts to ensure Chat2Query really nails it. By using advanced techniques like Chain of Thought (COT) and Retrieval Augmented Generation (RAG), we guide the system to reason through the question step-by-step, ensuring the most accurate SQL possible.<\/p>\n\n\n\n
\n- Why it matters:<\/strong> Our COT + RAG combo helps Chat2Query consistently crush benchmarks like Spider and BIRD. It\u2019s the secret sauce behind those impressive scores.<\/li>\n<\/ul>\n\n\n\n
<\/figure>\n<\/div>\n\n\nFigure 3. The primary steps involved in prompt engineering<\/em>.<\/p>\n\n\n\nFine-Tuning with Post-Processing<\/h3>\n\n\n\n
Even with advanced technologies like LLMs, sometimes hallucinations can occur. That\u2019s why we use a multi-agent collaboration mechanism during post-processing. This system works like a team of experts reviewing the raw SQL output, identifying any inconsistencies, and refining the query to improve its accuracy.<\/p>\n\n\n\n
The multi-agent collaboration mechanism helps ensure that even if a model misses something, other components of the system step in to catch those issues and make necessary adjustments. This added layer of refinement significantly enhances the reliability of the SQL queries produced by Chat2Query.<\/p>\n\n\n\n
\n- Why it matters:<\/strong> This post-processing mechanism, along with the multi-agent system, increases the overall accuracy of SQL queries by 2-4%. It helps reduce errors and ensure that the output is stable, consistent, and ready for actionable insights.<\/li>\n<\/ul>\n\n\n\n
<\/figure>\n<\/div>\n\n\nFigure 4. Post-processing in action<\/em>.<\/p>\n\n\n\n<\/span>Real-World Use Cases: How Chat2Query Powers Smarter Decisions<\/span><\/h2>\n\n\n\nNow, let\u2019s talk about how Chat2Query is making a real difference for businesses:<\/p>\n\n\n\n
\n- Sales Performance:<\/strong> Ask \u201cHow much did our sales increase this month compared to last month?\u201d and instantly get the data you need to adjust your sales strategy. No more waiting on reports or pulling data manually.<\/li>\n\n\n\n
- Customer Insights:<\/strong> Quickly discover what your customers are saying. Ask \u201cWhat were the most common customer complaints this month?\u201d to identify areas that need attention and improve your service.<\/li>\n\n\n\n
- Supply Chain Optimization: <\/strong>Stay on top of your inventory by asking \u201cWhich products are below the safety stock level?\u201d or \u201cWhy did we experience delays last week?\u201d to adjust your supply chain strategy on the fly.<\/li>\n\n\n\n
- Financial Reporting:<\/strong> Need to know what caused fluctuations in the market? Simply ask, \u201cWhat were the main factors driving market changes this week?\u201d <\/strong>and get detailed insights to stay ahead of the competition.<\/li>\n<\/ul>\n\n\n\n
<\/span>Looking Ahead: The Future of Chat2Query<\/span><\/h2>\n\n\n\nChat2Query is still evolving, and we\u2019re continuously working to improve it. We\u2019re exploring new techniques and optimizations to make Text2SQL even more accurate and powerful. Our goal is to keep simplifying the way businesses interact with their data, making complex analysis accessible to anyone, regardless of their technical background.<\/p>\n\n\n\n
<\/span>Get Started with Chat2Query Today<\/span><\/h2>\n\n\n\nReady to see the magic of Chat2Query for yourself? It\u2019s time to make your data work harder for you.<\/p>\n\n\n\n
\n- Try TiDB Cloud for Free:<\/strong> Experience the power of Chat2Query with TiDB Cloud<\/a> and see how simple data analysis can be.<\/li>\n\n\n\n
- Schedule a Demo<\/strong>: Want to learn more? Book a session<\/a> with one of our experts to see how Chat2Query can transform your business.<\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"
What if you could interact with your data just like you would with a colleague? No need for complicated SQL queries or advanced data analysis tools\u2014just ask your data a question and get an immediate, clear answer. Sounds like magic, right? Well, it\u2019s not. It\u2019s TiDB’s Chat2Query, and it\u2019s changing the game in how businesses […]<\/p>\n","protected":false},"author":286,"featured_media":24871,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"ub_ctt_via":"","footnotes":""},"categories":[13],"tags":[356,357,360,359,298,111],"class_list":["post-24862","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-product","tag-chat2query","tag-data-analysis","tag-llms","tag-prompt-engineering","tag-rag","tag-tidb"],"acf":[],"featured_image_src":"https:\/\/static.pingcap.com\/files\/2025\/01\/14143944\/tidb_feature_1800x600-1-2.png","author_info":{"display_name":"Jway Wan","author_link":"https:\/\/www.pingcap.com\/ko\/blog\/author\/jwan\/"},"yoast_head":"\n
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Chat2Query: Instant Business Insights, No SQL 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First, Chat2Query needs to get familiar with your data. To do this, it analyzes your database using both relational and vector databases. This hybrid approach allows Chat2Query to understand the structure of the database and the relationships between tables, columns, and entities. The vector database is key for enriching the context by storing more complex, high-dimensional data that helps Chat2Query better understand the relationships between data points. The more Chat2Query knows about the data, the more accurate and insightful the responses.<\/p>\n\n\n\n
2. Ask Your Questions<\/strong><\/h3>\n\n\n\nOnce the data context is enriched, you can start asking questions. Chat2Query then converts your question into a SQL query, pulls the data, and gives you your answer\u2014usually with a handy chart or graph to make it even easier to digest.<\/p>\n\n\n
\n<\/figure>\n<\/div>\n\n\nFigure 1. How Chat2Query works<\/em>.<\/p>\n\n\n\n<\/span>Why Chat2Query Works So Well<\/span><\/h2>\n\n\n\nFor Chat2Query to function effectively, it needs to understand the database schema. That\u2019s where \u201cUnderstand db\u201d comes in. It\u2019s like giving Chat2Query a roadmap of your data\u2014helping it understand relationships between tables, columns, and entities.<\/p>\n\n\n\n
\n- Why it matters:<\/strong> This step alone can improve SQL accuracy by 2-3% on benchmarks like Spider<\/a>. A small increase, but a big deal when you\u2019re dealing with large datasets.<\/li>\n<\/ul>\n\n\n\n
<\/figure>\n<\/div>\n\n\nFigure 2. Understand db<\/em>.<\/p>\n\n\n\nPerfecting Prompts: Prompt Engineering<\/h3>\n\n\n\n
You can\u2019t just throw any question at a system and expect it to work perfectly. So, we engineer our prompts to ensure Chat2Query really nails it. By using advanced techniques like Chain of Thought (COT) and Retrieval Augmented Generation (RAG), we guide the system to reason through the question step-by-step, ensuring the most accurate SQL possible.<\/p>\n\n\n\n
\n- Why it matters:<\/strong> Our COT + RAG combo helps Chat2Query consistently crush benchmarks like Spider and BIRD. It\u2019s the secret sauce behind those impressive scores.<\/li>\n<\/ul>\n\n\n\n
<\/figure>\n<\/div>\n\n\nFigure 3. The primary steps involved in prompt engineering<\/em>.<\/p>\n\n\n\nFine-Tuning with Post-Processing<\/h3>\n\n\n\n
Even with advanced technologies like LLMs, sometimes hallucinations can occur. That\u2019s why we use a multi-agent collaboration mechanism during post-processing. This system works like a team of experts reviewing the raw SQL output, identifying any inconsistencies, and refining the query to improve its accuracy.<\/p>\n\n\n\n
The multi-agent collaboration mechanism helps ensure that even if a model misses something, other components of the system step in to catch those issues and make necessary adjustments. This added layer of refinement significantly enhances the reliability of the SQL queries produced by Chat2Query.<\/p>\n\n\n\n
\n- Why it matters:<\/strong> This post-processing mechanism, along with the multi-agent system, increases the overall accuracy of SQL queries by 2-4%. It helps reduce errors and ensure that the output is stable, consistent, and ready for actionable insights.<\/li>\n<\/ul>\n\n\n\n
<\/figure>\n<\/div>\n\n\nFigure 4. Post-processing in action<\/em>.<\/p>\n\n\n\n<\/span>Real-World Use Cases: How Chat2Query Powers Smarter Decisions<\/span><\/h2>\n\n\n\nNow, let\u2019s talk about how Chat2Query is making a real difference for businesses:<\/p>\n\n\n\n
\n- Sales Performance:<\/strong> Ask \u201cHow much did our sales increase this month compared to last month?\u201d and instantly get the data you need to adjust your sales strategy. No more waiting on reports or pulling data manually.<\/li>\n\n\n\n
- Customer Insights:<\/strong> Quickly discover what your customers are saying. Ask \u201cWhat were the most common customer complaints this month?\u201d to identify areas that need attention and improve your service.<\/li>\n\n\n\n
- Supply Chain Optimization: <\/strong>Stay on top of your inventory by asking \u201cWhich products are below the safety stock level?\u201d or \u201cWhy did we experience delays last week?\u201d to adjust your supply chain strategy on the fly.<\/li>\n\n\n\n
- Financial Reporting:<\/strong> Need to know what caused fluctuations in the market? Simply ask, \u201cWhat were the main factors driving market changes this week?\u201d <\/strong>and get detailed insights to stay ahead of the competition.<\/li>\n<\/ul>\n\n\n\n
<\/span>Looking Ahead: The Future of Chat2Query<\/span><\/h2>\n\n\n\nChat2Query is still evolving, and we\u2019re continuously working to improve it. We\u2019re exploring new techniques and optimizations to make Text2SQL even more accurate and powerful. Our goal is to keep simplifying the way businesses interact with their data, making complex analysis accessible to anyone, regardless of their technical background.<\/p>\n\n\n\n
<\/span>Get Started with Chat2Query Today<\/span><\/h2>\n\n\n\nReady to see the magic of Chat2Query for yourself? It\u2019s time to make your data work harder for you.<\/p>\n\n\n\n
\n- Try TiDB Cloud for Free:<\/strong> Experience the power of Chat2Query with TiDB Cloud<\/a> and see how simple data analysis can be.<\/li>\n\n\n\n
- Schedule a Demo<\/strong>: Want to learn more? Book a session<\/a> with one of our experts to see how Chat2Query can transform your business.<\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"
What if you could interact with your data just like you would with a colleague? No need for complicated SQL queries or advanced data analysis tools\u2014just ask your data a question and get an immediate, clear answer. Sounds like magic, right? Well, it\u2019s not. It\u2019s TiDB’s Chat2Query, and it\u2019s changing the game in how businesses […]<\/p>\n","protected":false},"author":286,"featured_media":24871,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"ub_ctt_via":"","footnotes":""},"categories":[13],"tags":[356,357,360,359,298,111],"class_list":["post-24862","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-product","tag-chat2query","tag-data-analysis","tag-llms","tag-prompt-engineering","tag-rag","tag-tidb"],"acf":[],"featured_image_src":"https:\/\/static.pingcap.com\/files\/2025\/01\/14143944\/tidb_feature_1800x600-1-2.png","author_info":{"display_name":"Jway Wan","author_link":"https:\/\/www.pingcap.com\/ko\/blog\/author\/jwan\/"},"yoast_head":"\n
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Chat2Query: Instant Business Insights, No SQL Required<\/h5><\/div><\/a>","_links":{"self":[{"href":"https:\/\/www.pingcap.com\/ko\/wp-json\/wp\/v2\/posts\/24862","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pingcap.com\/ko\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.pingcap.com\/ko\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.pingcap.com\/ko\/wp-json\/wp\/v2\/users\/286"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pingcap.com\/ko\/wp-json\/wp\/v2\/comments?post=24862"}],"version-history":[{"count":19,"href":"https:\/\/www.pingcap.com\/ko\/wp-json\/wp\/v2\/posts\/24862\/revisions"}],"predecessor-version":[{"id":24891,"href":"https:\/\/www.pingcap.com\/ko\/wp-json\/wp\/v2\/posts\/24862\/revisions\/24891"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.pingcap.com\/ko\/wp-json\/wp\/v2\/media\/24871"}],"wp:attachment":[{"href":"https:\/\/www.pingcap.com\/ko\/wp-json\/wp\/v2\/media?parent=24862"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.pingcap.com\/ko\/wp-json\/wp\/v2\/categories?post=24862"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.pingcap.com\/ko\/wp-json\/wp\/v2\/tags?post=24862"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/figure>\n<\/div>\n\n\nFigure 1. How Chat2Query works<\/em>.<\/p>\n\n\n\n For Chat2Query to function effectively, it needs to understand the database schema. That\u2019s where \u201cUnderstand db\u201d comes in. It\u2019s like giving Chat2Query a roadmap of your data\u2014helping it understand relationships between tables, columns, and entities.<\/p>\n\n\n\n Figure 2. Understand db<\/em>.<\/p>\n\n\n\n You can\u2019t just throw any question at a system and expect it to work perfectly. So, we engineer our prompts to ensure Chat2Query really nails it. By using advanced techniques like Chain of Thought (COT) and Retrieval Augmented Generation (RAG), we guide the system to reason through the question step-by-step, ensuring the most accurate SQL possible.<\/p>\n\n\n\n Figure 3. The primary steps involved in prompt engineering<\/em>.<\/p>\n\n\n\n Even with advanced technologies like LLMs, sometimes hallucinations can occur. That\u2019s why we use a multi-agent collaboration mechanism during post-processing. This system works like a team of experts reviewing the raw SQL output, identifying any inconsistencies, and refining the query to improve its accuracy.<\/p>\n\n\n\n The multi-agent collaboration mechanism helps ensure that even if a model misses something, other components of the system step in to catch those issues and make necessary adjustments. This added layer of refinement significantly enhances the reliability of the SQL queries produced by Chat2Query.<\/p>\n\n\n\n Figure 4. Post-processing in action<\/em>.<\/p>\n\n\n\n Now, let\u2019s talk about how Chat2Query is making a real difference for businesses:<\/p>\n\n\n\n Chat2Query is still evolving, and we\u2019re continuously working to improve it. We\u2019re exploring new techniques and optimizations to make Text2SQL even more accurate and powerful. Our goal is to keep simplifying the way businesses interact with their data, making complex analysis accessible to anyone, regardless of their technical background.<\/p>\n\n\n\n Ready to see the magic of Chat2Query for yourself? It\u2019s time to make your data work harder for you.<\/p>\n\n\n\n What if you could interact with your data just like you would with a colleague? No need for complicated SQL queries or advanced data analysis tools\u2014just ask your data a question and get an immediate, clear answer. Sounds like magic, right? Well, it\u2019s not. It\u2019s TiDB’s Chat2Query, and it\u2019s changing the game in how businesses […]<\/p>\n","protected":false},"author":286,"featured_media":24871,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"ub_ctt_via":"","footnotes":""},"categories":[13],"tags":[356,357,360,359,298,111],"class_list":["post-24862","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-product","tag-chat2query","tag-data-analysis","tag-llms","tag-prompt-engineering","tag-rag","tag-tidb"],"acf":[],"featured_image_src":"https:\/\/static.pingcap.com\/files\/2025\/01\/14143944\/tidb_feature_1800x600-1-2.png","author_info":{"display_name":"Jway Wan","author_link":"https:\/\/www.pingcap.com\/ko\/blog\/author\/jwan\/"},"yoast_head":"\n<\/span>Why Chat2Query Works So Well<\/span><\/h2>\n\n\n\n
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<\/figure>\n<\/div>\n\n\nPerfecting Prompts: Prompt Engineering<\/h3>\n\n\n\n
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<\/figure>\n<\/div>\n\n\nFine-Tuning with Post-Processing<\/h3>\n\n\n\n
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<\/figure>\n<\/div>\n\n\n<\/span>Real-World Use Cases: How Chat2Query Powers Smarter Decisions<\/span><\/h2>\n\n\n\n
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<\/span>Looking Ahead: The Future of Chat2Query<\/span><\/h2>\n\n\n\n
<\/span>Get Started with Chat2Query Today<\/span><\/h2>\n\n\n\n
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