ai · 2026
SampleDocument Assistant (RAG) Reference
A retrieval-augmented assistant that answers questions from a private document set and cites the passages it used.
assistant.example.com/chat
- Category
- ai
- Year
- 2026
- Stack
- Python · LLM APIs · RAG · Vector search · Node.js
Overview
A reference implementation of the AI feature we get asked for most: a grounded assistant over internal documents that shows its sources instead of guessing.
- Demo record — an internal reference build, not a client engagement.
- Demonstrates the approach; answer quality depends entirely on the documents supplied.
The problem
Teams want to ask questions of long documents — policies, manuals, research — but a plain chatbot produces confident answers with no way to verify them.
The solution
A retrieval pipeline with chunking and embeddings over the document set, model answers constrained to retrieved context, and inline citations back to the source passages, with an honest fallback when nothing relevant is found.
Key features
- Document ingestion with a deliberate chunking strategy
- Embedding-based retrieval with metadata filters
- Grounded answers with inline citations
- Streaming responses and clear failure states
- Cost and latency instrumentation
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