Enterprise RAG: Making AI Trustworthy With Retrieval

The author explains that retrieval augmented generation (RAG) fixes the hallucination problem of large language models by basing answers on real documents and adding citations.

The article walks through the six‑step RAG process:

1. User query

2. Vector embedding of the query

3. Database retrieval of relevant passages

4. Context assembly (putting the passages together)

5. Generation of the answer

6. Citation of the sources

It then compares RAG with fine tuning, showing that RAG offers:

• Knowledge freshness – it can use up‑to‑date information

• Lower cost

• Less risk of hallucinations

• Better data privacy

• Easier deployment

The guide also gives practical advice on:

• Choosing components (embedders, retrievers, etc.)

• Using hybrid search (combining vector and keyword search)

• Permission‑aware retrieval (respecting access rules)

• Chunking strategy (how to split documents)

• Evaluation metrics such as context precision, faithfulness, and citation accuracy

Finally, it notes a new trend called agentic RAG, where autonomous agents call the RAG pipeline to perform multi‑step reasoning.