RAG Document Q&A
Semantic search & cited answers over any document library
Tech stack
Architecture highlights
- Chunk → embed → upsert pipeline into Pinecone
- Retrieval-augmented GPT-4 generation with citations
- Streaming Next.js frontend with conversation memory
The problem
Teams sit on gigabytes of PDFs, docs, and notes but can't get trustworthy answers without reading everything manually.
What I built
A full-stack RAG application (Next.js + LangChain + Pinecone) that ingests documents, embeds them, and answers questions with GPT-4 — every answer backed by source citations.
Key features
- Drag-and-drop ingestion for PDF, DOCX, TXT & Markdown
- Intelligent chunking + OpenAI text-embedding-3-small
- Semantic search over Pinecone with GPT-4 synthesis
- Cited answers, real-time queries & conversation history
The hardest challenge
Eliminating hallucinations while keeping answers fast — solved with tuned chunking, top-k retrieval, and strict source-grounded prompting that refuses to answer beyond the documents.
The full story
Problem
Generic chatbots hallucinate. Teams needed answers they could trust and verify against their own documents.
Research
I tested chunking strategies, embedding models and retrieval parameters to maximise answer accuracy while minimising latency and token cost.
Architecture
A Next.js frontend with a LangChain pipeline: document ingestion → intelligent chunking → OpenAI embeddings → Pinecone vector store → GPT-4 synthesis with source grounding.
Implementation
I built drag-and-drop ingestion for multiple file types, streaming responses, conversation history, and strict source-grounded prompts that decline to answer beyond the provided documents.
Result
Users get accurate, source-cited answers in seconds across any document library, with a zero-hallucination policy enforced at the prompt layer.
Lessons learned
Retrieval quality — not the model — is where RAG lives or dies. Time spent on chunking and grounding beat any amount of prompt cleverness.
Outcome
- Accurate, source-cited answers in seconds
- Zero-hallucination policy via grounded prompts
- Reusable ingestion pipeline for any doc type