In Progress
Self-Improving RAG System
An autonomous Retrieval-Augmented Generation platform that evaluates its own answers, identifies knowledge gaps, and expands the corpus without human hand-holding.
System Overview
The system orchestrates a loop of question generation, answer evaluation, and document expansion. It detects low-confidence responses, spins up targeted web crawlers or API fetchers, and rewrites embeddings to close the knowledge gap—no manual curation required.
Technical Highlights
- Hybrid retrieval with ColBERT + dense embeddings for precision and recall.
- Evaluation agents score answers with rubric-driven critique and hallucination detection.
- Automated corpus expansion via task-specific crawlers and PDF parsing flows.
- Feedback loop persisted in PostgreSQL + pgvector with lineage metadata for audits.
Why it Matters
Traditional RAG systems decay quickly as source material evolves. By instrumenting evaluation and ingestion agents, this platform continuously improves coverage and answer quality—turning RAG from a static knowledge base into a living system.