LLM Development9 min read1touch.ai Research

RAG Architecture for Enterprise Knowledge: Making LLMs Company-Aware

Retrieval-augmented generation connects LLMs to your internal knowledge. Here’s how to design RAG that is permission-aware, citable, and production-ready.

General-purpose LLMs do not know your contracts, playbooks, or product specs. RAG (retrieval-augmented generation) feeds the model the right passages at answer time.

Core RAG components

Document ingestion, chunking, embeddings, vector index, retriever, re-ranker, prompt assembly, and citation of sources.

Enterprise requirements

Permission-aware retrieval, PII handling, evaluation of faithfulness, refresh pipelines when docs change, and clear UX for “I don’t know.”

Common failure modes

Junk in the corpus, chunks that are too large or too small, weak retrieval, and no feedback loop from users.

Beyond RAG

For high-volume domain tasks, combine RAG with light fine-tuning and tool-using agents.

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LLM Development

LLM Development for Enterprise: Fine-Tuning, RAG, and Production Delivery

A practical overview of enterprise LLM development — when to fine-tune, when to use RAG, evaluation …

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