Guided Demo

RAG Document Assistant Demo

What It Demonstrates

A document assistant flow with sample document selection, retrieved context, source-grounded answer generation, and productionization notes. This walkthrough covers the key architecture decisions behind building a production RAG system.

Who It Is For

Startups, internal teams, and agencies building document Q&A, knowledge base assistants, or retrieval-backed LLM apps.

Demo Flow

1. User selects a sample document (PDF or web page)
2. Document is chunked and embedded into Qdrant
3. User asks a question
4. System retrieves relevant chunks and generates a source-grounded answer
5. Display answer with source citations

Architecture

User -> Frontend -> API -> Retriever -> Vector Store -> LLM -> Source-grounded answer

Tech Stack

RAG, vector search, Qdrant, FastAPI, Python, LLM APIs, embeddings.

Productionization Notes

• Chunking strategy: Chunk size, overlap, and metadata choices affect retrieval quality
• Embedding model: Tradeoffs between speed, cost, and retrieval accuracy
• Vector store: Qdrant vs FAISS vs managed vector DB for different scale requirements
• Production concerns: Authentication, rate limiting, error handling, monitoring, retries, cost controls

CTA

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