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engineering notes · Practical business guide

RAG vs Fine-Tuning: Choosing a Business AI Architecture

By SyntaxLab · 2 min read

Choose retrieval or fine-tuning based on changing business facts, output behavior, permission boundaries, and the evidence your application must provide.

RAG vs Fine-Tuning: Choosing a Business AI Architecture

Retrieval-augmented generation supplies relevant source material at answer time. Fine-tuning changes model behavior through training examples. The decision should follow the problem: frequently changing policies and permissioned documents call for accessible sources; a repeated formatting or classification behavior may benefit from training.

A practical scenario

A company handbook changes several times a year and differs by region. Retrieving the current approved policy makes source updates and citations manageable. Training that text into a model would not create a reliable mechanism for deleting an obsolete policy or enforcing employee-specific access.

Design the first version

Start with a baseline prompt and a test set. Add retrieval when errors come from missing business facts. Consider fine-tuning only after identifying a stable behavior gap and collecting suitable examples with permission. These approaches can be combined, but combining them adds evaluation and maintenance work.

What to test and measure

Compare factual support, format adherence, latency, ongoing costs, and update effort. Test an obsolete policy and a restricted document explicitly. Do not treat fine-tuning as an access-control system or retrieval as a guarantee of correctness. Choose the architecture that makes the important failures observable and correctable.

Questions to resolve before commissioning

  • Which source system owns the facts used in this workflow?
  • Who reviews exceptions and corrects inaccurate output?
  • What baseline and acceptance criteria will determine whether the pilot is useful?
  • What should the user do when a source, tool, or device is unavailable?

Explore the implementation

This is a planning guide, not a report of measured client results. Examples are illustrative. Explore the related SyntaxLab demo to discuss the interaction, then use your own records and acceptance criteria for a production pilot. Discuss a scoped project or review our AI automation services.

Further reading

Read Microsoft guidance on evaluating RAG answers for technical background. Continue with RAG Document Ingestion: A Checklist for Reliable Search.