Part 8/12:

• Context Window Solutions: For large schemas, schema filtering minimizes the context fed into language models, addressing context window limitations.

The system also incorporates an iterative, cycle-based process—if the generated SQL encounters errors, an error correction model, trained on failure cases, refines the query dynamically.

Continuous Learning & Performance Metrics

A key innovation is the system's ability to learn from interactions:

• Log errors, corrections, and user feedback in long-term memory.

• Use these insights to retrain and fine-tune models periodically.

• Improve SQL generation accuracy (initially from 20% to 70%) and reliability (from lower initial values to 90%).