TalkToData
Natural Language SQL.
AI-powered SQL query interface that converts plain English to SQL. Supports PostgreSQL, MySQL, SQLite, SQL Server, and MongoDB with AI-driven query explanations and error fixing.
- STACK
- 4 tools
- STATUS
- Live
- VIDEO
- 34s
- OUTCOMES
- 5
The challenge
Non-technical users need database insights but SQL is a barrier. Existing solutions are expensive (Text-to-SQL SaaS tools) or require complex setup. The goal was a lightweight, self-service tool that handles the 5 most common database dialects with zero configuration.
The solution
Built a Next.js app with a Next.js API route that sends a carefully engineered prompt to Groq's llama-3.1 model. The prompt includes the user's schema context, target dialect, and few-shot SQL examples to constrain output to valid queries. A second AI call generates a plain-English explanation of the query. A third call handles error recovery: if the SQL fails syntax validation, it feeds the error back to the model for correction.
ARCHITECTURE
IMPACT
- Supports 5 database dialects, and users switch between them with one click
- Sub-200ms SQL generation via Groq's inference speed (fastest LLM API for this use case)
- Error recovery loop fixes invalid SQL automatically, so users rarely see raw errors
- Deployed on Vercel with zero cold-start latency for the frontend
- Schema-aware prompting means column names and table relationships are respected in output
WHAT I LEARNED
- Few-shot examples in the prompt reduced SQL syntax errors by ~70% vs. zero-shot. The model needs to see the dialect in action
- Groq's llama-3.1-8b-instant was 3 to 5× faster than GPT-3.5 for this task and free-tier friendly, making it the right choice for a public demo
- Streaming the AI response character-by-character felt much faster to users than waiting for the full SQL, even though total latency was the same