Enterprise RAG Pipeline

Project Summary

Designed and deployed a production RAG (Retrieval-Augmented Generation) system for enterprise knowledge management, reducing time-to-answer for engineering questions from hours to seconds.

Problem Statement

• Engineers spending 30% of time searching across fragmented documentation
• Institutional knowledge siloed in Slack, Confluence, and individual notebooks
• New team members requiring 3+ months to become productive
• Critical information often discovered only through tribal knowledge

System Architecture

Multi-stage retrieval pipeline with hybrid search (dense + sparse), document chunking with semantic boundaries, and GPT-4 for answer generation. Vector store on Pinecone with metadata filtering, FastAPI backend, and React frontend. Deployed on Docker with Redis caching for frequent queries.

Model & Approach

• Implemented hierarchical chunking strategy respecting document structure
• Built custom embedding fine-tuning pipeline on internal documentation
• Designed prompt engineering framework with few-shot examples and guardrails
• Created feedback loop for continuous retrieval quality improvement

MLOps & Deployment

• Automated document ingestion pipeline from multiple sources
• A/B testing framework for retrieval strategies and prompts
• Token usage monitoring and cost optimization
• Guardrails for hallucination detection and source verification
• User feedback integration for retrieval ranking improvements

Results & Impact

Lessons Learned

• Retrieval quality matters more than generation model sophistication
• Chunking strategy dramatically affects answer relevance
• User feedback is essential for understanding real-world failure modes
• Cost management requires careful attention to token usage patterns