When Not to Use RAG

We got into this issue a lot when we developed out own Chatbot and Voice AI solutions. Customers come in with a lot of requirements and usually want AI to literally do the work of a human. This usually means they give us a whole lot of data to train the AI on but what is a whole lot really? 10 FAQs? 100?

Seeing as how RAG is all the hype around AI systems so much so that there are libraries built just to do this raising millions of dollars in funding, we jumped to this too. At the core of it Retrieval-Augmented Generation, or RAG, has become the default answer to almost every AI problem lately.

Need an internal chatbot? Use RAG. Need accurate answers? Use RAG. Need AI to read documents? Use RAG.

And while RAG is powerful, using it everywhere is a mistake. Here are some of the cases where you should not use RAG.

When the knowledge is small, stable, and well-defined

If your data fits comfortably in the model context and does not change often, RAG is overkill.

Examples:

• Product FAQs with 50 to 200 questions
• API documentation for one service
• Company policies that change once every few months

In these cases, embedding documents, maintaining a vector database, and handling retrieval errors adds complexity without real benefit.

What works better is Inline context or fine-tuning. You can store content as plain markdown or Inject it directly into the prompt.

When correctness matters more than coverage

RAG is probabilistic. It retrieves based on similarity, not truth.

This is dangerous when the answers must be exact, Partial answers are worse than no answers and The cost of being wrong is high.

Examples:

• Payment systems
• Authentication and authorization logic
• Financial calculations
• Medical or legal workflows

What works better is a Deterministic system with AI as a helper, not a decider. This is exactly how we handle our voice AI pipeline at Rhythmiq.

When your problem is actually reasoning, not retrieval

Many teams add RAG when the real issue is reasoning quality, not missing information. If the answers are shallow and logic is inconsistent, then RAG does not fix reasoning. It just adds more text.

Common mistakes

• Using RAG to fix bad prompts
• Adding more documents instead of better instructions
• Assuming more context equals better answers

Sometimes the model needs structure, not knowledge.

Final thoughts

RAG is a powerful tool, but it is not a default architecture.

Use RAG when:

• Knowledge is large
• Changes frequently
• Cannot fit in context

Avoid RAG when:

• Data is small and stable
• Correctness is critical
• Logic matters more than information

Well, that’s it for now. Until next time!

— Ray