Document Q&A Chatbot

A Retrieval-Augmented Generation pipeline that lets users ask questions about PDF documents. The LLM answers from the source material rather than from its own parametric memory.

LLMs confidently make things up. For document Q&A, answers must come from the document — and irrelevant context in the prompt makes answers worse, not better.

1. 01

   Ingest

   Extracted text from PDFs and split it into overlapping chunks.

2. 02

   Index

   Generated vector embeddings and indexed them in a vector database for semantic search.

3. 03

   Retrieve & assemble

   Retrieved the most relevant chunks and formatted them into a structured prompt.

4. 04

   Chain & tune

   Chained retriever, prompt template, and LLM into one pipeline; tuned chunk size, overlap, and top-k to reduce irrelevant context.

• A working end-to-end RAG pipeline over arbitrary PDFs.
• Tuned chunking and top-k retrieval to keep irrelevant context out of prompts.

“Most RAG quality problems are retrieval problems. Time spent on chunking and top-k paid off more than prompt tweaking.”

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