AI Document Assistant.
Ask your documents anything.
An intelligent document processing and Q&A tool powered by retrieval-augmented generation (RAG). Users upload PDFs, contracts, or research papers and get accurate, cited answers in seconds — no prompt engineering required.
The challenge
A legal tech startup needed their paralegals to review 200+ page contracts in minutes instead of hours. Existing tools either required complex prompt engineering or hallucinated answers without source attribution. The team needed an AI assistant that was accurate, fast, and trustworthy — with every answer traced back to the specific paragraph it came from.
The approach
I built a retrieval-augmented generation (RAG) pipeline that chunks documents intelligently, embeds them into a Pinecone vector store, and retrieves the most relevant passages before generating answers with GPT-4. The system uses a hybrid search strategy combining semantic similarity with keyword matching for maximum recall.
Key technical decisions:
- Recursive text splitting — context-aware chunking that respects paragraph and section boundaries, preserving meaning across chunk boundaries
- Streaming responses — answers appear word-by-word via Server-Sent Events, reducing perceived latency from 8 seconds to under 1 second
- Citation anchoring — every claim links back to the source paragraph with page number, enabling one-click verification
- Multi-document conversations — users can query across multiple uploaded documents simultaneously with cross-reference support
- Token-aware context window — dynamic context assembly that maximizes relevant information within GPT-4's context limits
The outcome
- 94% answer accuracy verified against human expert reviews
- 3-second average response time (down from 15+ seconds without streaming)
- 75% reduction in contract review time for paralegals
- 2,500+ documents processed in the first month
- Zero hallucination incidents in production (citation-or-decline policy)
- $45K estimated monthly savings in billable review hours
Technical highlights
The embedding pipeline processes documents asynchronously using a job queue (BullMQ), enabling users to upload files and return later without waiting. Pinecone namespaces isolate each user's documents for privacy and performance. The system handles PDFs, DOCX, and plain text with automatic format detection.
A feedback loop captures user corrections, which are used to fine-tune the retrieval ranking model monthly. Comprehensive logging with LangSmith provides full trace visibility for debugging and quality assurance.