Enterprise NL2SQL Fine-Tuning System
An enterprise NL2SQL system that spans schema-aware data generation, LoRA tuning, validation, and execution-aware evaluation.
This project broadens the portfolio beyond RAG and shows that the same engineering mindset can be applied to model adaptation, evaluation, and enterprise data workflows.
Overview
Generic text-to-SQL systems tend to break down in enterprise settings because they do not understand private schema design, organization-specific terminology, or production validation constraints.
This project addresses that gap by treating NL2SQL as a full pipeline problem:
- generate training samples from database metadata
- tune models with LoRA-style adaptation
- validate SQL automatically
- evaluate not just text similarity, but execution behavior
That makes the project much closer to a deployable enterprise workflow than a one-off fine-tuning experiment.
Product and System Shape
The system includes both model-related and operator-facing layers:
- a data-generation application with configuration controls
- schema-aware sample creation across database structures
- progress tracking with FastAPI and WebSocket updates
- LoRA and QLoRA training workflows
- evaluation that includes execution-oriented checks
This matters because it shows that model tuning was treated as an engineering workflow, not only as notebook research.
Why This Project Stands Out
Among portfolio projects, this one is valuable because it signals:
- enterprise workflow understanding
- data-centric thinking
- model adaptation experience beyond prompting
- evaluation discipline tied to real task quality
For applied AI and AI engineer roles, that helps balance a portfolio that might otherwise look too RAG-heavy.
Deployment and Demo Strategy
The best way to present this project publicly is not to expose unrestricted training. A better demo format is:
- show a sample schema
- generate example NL2SQL pairs
- run a small validation workflow
- display evaluation summaries and a few query examples
That gives visitors something concrete to interact with while keeping the heavier training path private or offline.
Recommended public demo format
A public demo should focus on schema preview, sample data generation, and a lightweight validation run instead of full open training. That keeps the experience interactive while avoiding long-running or expensive model-tuning behavior in the portfolio itself.
What This Project Signals
- schema-aware AI system design
- full-stack support around model workflows
- execution-aware evaluation mindset
- practical adaptation of LLMs to enterprise data problems