AI product case study
RAG Knowledge Assistant
Integrated chatbot pattern for answering domain questions from private documents, datasets, and structured knowledge. Built around retrieval-augmented generation, source-aware responses, and quality-controlled LLM delivery.
- Problem
- Internal knowledge is usually scattered across documents, spreadsheets, tickets, and tools. Generic prompts do not preserve context or trust.
- Role
- Architecture, ingestion flow, retrieval strategy, chatbot integration, prompt/context design, and evaluation loop.
- Stack
- TypeScript, Node.js, embeddings, vector search, Claude/Codex workflows, LLM gateway patterns, and chatbot UI.
Architecture
- Document ingestion pipeline with parsing, normalization, chunking, metadata extraction, and embedding generation.
- Retrieval layer using semantic search, optional filters, and context assembly before each LLM call.
- Chatbot API designed to keep private context server-side while returning source-aware answers to the interface.
- Model abstraction that allows routing between Claude, OpenAI-compatible providers, or local model gateways by use case.
Engineering Highlights
- Answers grounded in retrieved context instead of one-off prompt engineering.
- Quality gates with test prompts, expected-source checks, and regression evaluation for key knowledge paths.
- Auditable traces for question, retrieved chunks, model response, and final answer.
- Reusable architecture for support bots, internal knowledge assistants, onboarding flows, and domain-specific copilots.