When to use RAG in a business project

RAG, an acronym for recovery-augmented generation, is useful when an application needs to respond with knowledge that changes, belongs to the company, or must be cited. Before generating, the system searches for relevant snippets and provides them to the model. This doesn't eliminate errors, but it makes the response more informed and auditable.

Use RAG for queryable knowledge

Policies, manuals, contracts, catalogues, resolved tickets and technical documentation are candidates. The content needs to have textual value, defined permission and some organization. AWS describes RAG as a way to incorporate external sources into the model context without retraining it.

RAG is especially useful when the answer must point to the source or when documents change frequently. Updating an index is often simpler than retraining a model.

Do not use RAG for any data

For balance, order status, and real-time metrics, query the API or bank with a vetted tool. For exact calculations and rules, use code. For literal searches in a few documents, a conventional engine may be sufficient. RAG should not replace a structured source just because the project involves AI.

Fine-tuning solves another problem. It helps adjust behavior, format or recurring pattern; It's not the best way to keep business facts up to date. RAG and fine-tuning can coexist.

Quality depends on recovery

Prepare documents, remove duplicates, preserve titles and record version, owner and validity. Break the content into coherent chunks and test real questions. Evaluate whether the system retrieves the right font before evaluating the writing. A bad answer can be born from poor search, insufficient context, or inadequate generation.

Permissions come before search

Filter documents according to identity before sending them to the template. Don't retrieve everything and then hide the answer. Record which sources were used and avoid putting secrets, credentials, or unnecessary data in the index.

Define how to deal with absence

The system must admit that it has not found sufficient basis. Configure threshold, uncertainty message and forwarding. Forcing a response turns model fluency into operational risk.

Good design measures retrieval accuracy, response support across sources, freshness, latency, cost, and user feedback. See the article about corporate RAG for applications and data protection in AI for controls.