An internal AI assistant is useful only when people can trust where its answers came from. My enterprise RAG project focused on making retrieval and evidence part of the product experience rather than an implementation detail.
The system retrieves relevant internal knowledge, generates an answer grounded in that context, and attaches citations so a user can inspect the source. Focused agents can then perform tasks such as search, summarization, and report generation.
Architecture principles
- Split documents carefully and retain source metadata.
- Retrieve a small, relevant evidence set before generation.
- Give agents narrow tools with explicit responsibilities.
- Make citations visible in the final response.
- Evaluate failure cases, especially missing or conflicting evidence.
The stack uses modern LLM tooling such as LangChain, LangGraph, LlamaIndex, vector search, and model APIs. The difficult work is not connecting an LLM to a database. It is designing boundaries that keep the system predictable and useful when the available knowledge is incomplete.
This project also informs my current work on evidence-verification and company knowledge systems.