RAG over enterprise data with Azure

RAG: grounding AI in your data

Language models know a lot about the world, but nothing about your company: your contracts, policies, products and history. Asking a raw model about these topics leads to hallucinations, made-up answers that look convincing. The solution is RAG (Retrieval-Augmented Generation): retrieve relevant snippets from your data and provide them as context so the model answers from real sources.

How RAG works

The basic flow has four steps:

1. Ingestion: your documents are split into chunks and converted into vectors (embeddings) that capture meaning.
2. Indexing: the vectors and text are stored in a search index, such as Azure AI Search.
3. Retrieval: on a user's question, the system finds the most relevant snippets by semantic similarity.
4. Generation: Azure OpenAI receives the question and snippets as context and generates the answer, ideally citing sources.

The result is a grounded, verifiable, up-to-date answer, without retraining the model when data changes.

Reference architecture on Azure

Hybrid search and why it matters

Purely vector search captures meaning but sometimes misses exact terms, such as codes and specific names. Hybrid search combines keyword and semantic search, and a reranker improves result relevance. This combination substantially raises answer quality across heterogeneous enterprise data.

Respecting permissions

An enterprise RAG must respect who can see what. Best practices include:

• Security filters in the index: each snippet carries the authorized groups, and search filters by the user.
• Entra ID integration: identity determines access.
• Segregation by area: indexes or filters per data domain.

Without this care, RAG can reproduce the same oversharing problem as Copilot.

Quality and evaluation

RAG is not "set and forget." Quality depends on continuous evaluation:

• Test sets: questions with expected answers to measure accuracy.
• Retrieval metrics: whether the right snippets were found.
• Generation metrics: faithfulness to the source and absence of hallucination.
• User feedback: flagging of bad answers.

Common adjustments include chunk size, the number retrieved and the instructions given to the model.

Common mistakes in RAG projects

• Chunks too large that dilute relevance.
• Ignoring hybrid search and missing exact terms.
• Not citing sources, reducing user trust.
• Forgetting permissions, creating exposure risk.
• Skipping evaluation, going to production without measuring quality.

Checklist for a RAG project

• Data sources selected and cleaned
• Chunking and embedding strategy defined
• Hybrid search and reranking configured
• Identity-based security filters applied
• Source citation in the answer
• Evaluation sets and metrics active

How RHC helps

As a Microsoft Solutions Partner, RHC designs and implements RAG solutions over your data with Azure AI Search and Azure OpenAI, handling ingestion, hybrid search, identity-based security filters, source citation and continuous quality evaluation. We deliver assistants that answer from your documents, with trust and control.

Key takeaways

• RAG grounds AI in your data, reducing hallucinations.
• Azure AI Search and Azure OpenAI form the architecture's base.
• Hybrid search and security filters raise quality and protection.
• Continuous evaluation is essential before and after production.