Text-to-SQL Agents: Insights On How AI Builders Evaluate Accuracy & Avoid Hallucinations
Community Insights #1: Text-to-SQL sounds great—until it hallucinates queries. AI builders share challenges, best practices, and tools for making it reliable.
The discussions and insights in this post originally appeared in the MLOps Community Newsletter as part of our weekly column contributions for the community (published every Tuesday and Thursday).
Want more deep dives into MLOps conversations, debates, and insights? Subscribe to the MLOps Community Newsletter (20,000+ builders)—delivered straight to your inbox every Tuesday and Thursday! 🚀
Imagine an LLM that writes SQL queries for your data team. Sounds like a game-changer, right? But what happens when it generates a perfectly structured SQL query that’s completely wrong?
From a recent thread of convos in the MLOps Community, a member kicked off a discussion on how teams are tackling one of the biggest hurdles in AI-powered analytics—ensuring that Text-to-SQL agents don’t hallucinate bad queries into production.
I thought this one would be great for our builders and readers; the insights and best practices from other readers were too good not to share.
Making sure the queries are correct, don't have hallucinations, and the results are based on good assumptions is still a big problem, even though these agents are supposed to free analysts from constantly writing SQL queries.
So, what does the community think? 🤔 Let’s see! ⬇️
⏩ Quick Summary:
Hallucinations, schema complexity, and the risk of incorrect queries make evaluating text-to-SQL agents challenging.
But hey, some best practices can help! Think metadata-driven schema selection, few-shot examples, and retrieval-based approaches. And please keep in mind the human touch—having a human in the loop for oversight and validation is crucial.
Plus, guardrails, versioning, and continuous feedback loops are key to preventing those pesky hallucinations and incorrect queries.
🔥 Key Takeaways from the Discussion:
⚠️ Key Challenges with Text-to-SQL
🚨 Hallucinated queries: LLMs might generate syntactically correct but semantically wrong queries that could lead to misleading results. “Bad queries are worse than no queries.”
🧠 Lack of schema knowledge: Users often don’t fully understand the underlying database structure, which makes it hard to phrase precise questions. (David: “Many use cases feel like a solution looking for a problem.”)
🛠️ Automating SQL query generation is harder than expected: Even skilled analysts struggle with new data ecosystems, and LLM-generated queries often require review—fully removing humans from the loop is unrealistic. (Ross Katz: “Guardrails are the hardest part—bad SQL that runs can be disastrous.”)
📊 How to Evaluate Text-to-SQL Accuracy
🔮 Execute the SQL & validate results: The most direct way to test accuracy is to run the generated queries and compare the results against a golden dataset. (Médéric Hurier)
📝 Use hand-crafted benchmarks: Create a set of typical business questions and their expected SQL queries. (Ross Katz, Alex Strick van Linschoten)
🔧 Leverage evaluation frameworks and tools:
DSPy: More focused on prompt fine-tuning, but some use its evaluation tools.
RAGA & QueryCraft: Popular frameworks for measuring query validity and semantic accuracy.
DeepEval: For benchmarking and tracking the LLM’s accuracy over time. (Ross Katz)
Faker: Generate fake SQL databases for testing. (Médéric Hurier)
🔄 Retrieval Augmented Generation (RAG) for schemas: Some prefer live database access to pull schema details dynamically and improve query accuracy. (Meb, Ross Katz)
🚀 Strategies to Improve Performance
📂 Metadata-driven schemas: Adding detailed descriptions, usage hints, column relationships, and sample values at the schema level significantly improves model accuracy. (Kahl Kritzinger)
👓 Pre-generated SQL views: Instead of dynamically constructing queries, some teams predefine SQL views for common questions, reducing error rates. (David, Ross Katz)
📖 Few-shot learning with analyst examples: Training the model on real-world SQL queries written by analysts greatly improves its ability to generate accurate responses. (Kahl Kritzinger)
🔎 Function calling for schema lookups: Consider dynamically fetching schema and sample queries during inference to improve context awareness. (Meb)
💬 Thoughts On “Fully Automated” Text-to-SQL
David: Many use cases feel like “a solution looking for a problem” given how much effort is needed to verify accuracy.
Ross Katz: Guardrails are the hardest part—bad SQL that runs can be disastrous. Maybe a human-in-the-loop approach is the way forward.
🔗 Bonus Resources Builders Shared:
Hamel.dev: Your AI Product Needs Evals: A great breakdown on how to evaluate LLM-generated SQL. (Alex Strick van Linschoten)
Text-to-SQL: The Ultimate Guide for 2025: A fresh perspective on the state of text-to-SQL in GenAI. (Médéric Hurier)
Lessons Learned from Text-to-SQL: Hands-on experiences from rolling out a text-to-SQL system. (Ross Katz)
LLM Eval For Text2SQL: Parlance Labs on Text-to-SQL. (Alex Strick van Linschoten)
🤔 The Big Question: Can Text-to-SQL Agents Fully Replace Analysts?
The general feeling is that it's not quite there yet. Even with retrieval-based systems and metadata-driven improvements, LLM-generated SQL still needs to be checked before it can be used to make decisions.
The best way to go about this is to use a hybrid model where the agent helps analysts by doing the simple stuff automatically, but humans are still in charge when things get complicated.
💬 What’s the worst hallucinated SQL query you’ve seen? Or, how is your team handling text-to-SQL reliability? Drop your experiences in the comments or join the discussion directly in the MLOps Community. 🚀