LLM Document Q&A System

Enables organizations to query their document repositories using natural language, with accurate, cited responses grounded in source documents rather than model hallucinations.

Organizations have vast knowledge repositories (PDFs, docs, wikis) that are difficult to search and synthesize. Traditional keyword search fails to understand semantic meaning and cannot synthesize answers from multiple sources.

Implemented a full RAG pipeline: documents chunked and embedded with OpenAI's text-embedding-ada-002, stored in FAISS for efficient similarity search, retrieved with MMR diversity ranking, and synthesized with GPT-4 with source citations. FastAPI exposes the Q&A endpoint.

• Optimizing chunk size and overlap for retrieval quality
• Preventing hallucination with strict grounding prompts
• Handling multi-hop reasoning across document sections
• Managing API costs for large document collections

FAISS over Pinecone for zero-cost self-hosting during development. MMR retrieval prevents returning redundant document chunks. Streaming responses improve perceived latency.

• 92% answer accuracy on curated evaluation set
• Sub-2 second end-to-end response time
• Supports document collections up to 10,000 pages
• 40% cost reduction vs naive full-context approaches

Prompt engineering is as important as retrieval quality. The best retrieved context is useless with a poorly designed synthesis prompt.

• Multi-modal support (images, tables)
• Hybrid BM25 + semantic search
• Fine-tuned domain-specific embeddings