This is Part 1 of a three-part series on building a production Text2SQL application using LLMs.

The Problems Data Teams Face

Six critical challenges that motivated this project:

  1. Access Management — Data governance remains a P0 requirement, but balancing security with flexibility is a constant tension
  2. High Request Volume — Engineers receive numerous data extraction requests; complex deep dives can take three weeks
  3. Dashboard Limitations — Pre-built dashboards can’t address all edge cases and evolving user needs
  4. Inefficient Search — Teams waste time searching repositories for existing queries rather than writing fresh ones
  5. Unfamiliar Data Sources — Engineers spend significant time understanding new data structures before they can analyze anything
  6. Pipeline Errors — Manual pipeline building is slow and error-prone, often requiring months

The Solution: Generative AI

Foundational Models

Large-scale ML models (GPT, Claude, BERT, LLaMA) trained on diverse data. Different models excel in different domains — Claude Sonnet excels in coding tasks, while Claude Haiku is optimized for conversational problems. Selecting the right model matters.

Agents

Agents function as “hands and legs” powered by LLM “brains.” They automatically determine optimal solutions using various tools and APIs.

Three key methodologies:

  • Chain of Thought — Sequential problem decomposition
  • ReACT — Reasoning combined with action determination
  • Return of Control — Handing results back to users for validation

The execution flow: understand the request, generate a plan, execute step-by-step, identify resources, invoke APIs, handle errors, and complete.

Current Market Limitations

Existing solutions like Amazon Q and Pyramid Analytics require users to specify exact table/column names, creating barriers to truly democratized data access. The goal is Text-to-SQL with minimal specificity requirements — a user should be able to ask a question in plain English.

Parts 2 and 3 cover the technical architecture and implementation details.