Thursday, August 27 at 6:00 PM GMT+1
Webinar
Text-to-SurQL: Prompt engineering for agentic retrieval
About this webinar
Most text-to-SQL agents fail for the same boring reason: someone pasted a schema into a system prompt months ago, a field was renamed since, and the model has been confidently writing queries against a database that no longer exists. No runbook fixes that, because documentation always rots at exactly the speed your schema changes. This session takes a different approach. Instead of maintaining the prompt as a document, we generate it as a query. SurrealDB can describe its own live structure, carry notes written specifically for the model inside its DDL, and assemble the finished prompt server side.
Speakers
Martin Schaer
AI Solutions Engineer at SurrealDB
In this session you'll learn
How to generate a schema block for your prompt from live database state, so it can never drift
Writing schema comments for a third audience: not the engine, not your colleagues, but the model
Grounding low-cardinality values in real data to stop the most common class of hallucination
Retrieving few-shot examples by meaning with vector search instead of hardcoding them
Why graph traversals are an easier generation target than JOINs, and what that removes
Enforcing safety with read-only roles, query plan checks, and timeouts rather than regex filters
Closing the loop so accepted queries improve the next generation