RAG.

Where RAG fits on a long engagement.

Retrieval quality determines answer quality, and almost every disappointing RAG system is a retrieval problem rather than a model problem. If the relevant passage is not in the context, no model will produce a correct answer, and it will usually produce a confident incorrect one instead.

Chunking is the decision that quietly decides retrieval quality. Chunks too small lose the context that makes a passage meaningful; too large and the embedding represents several topics and matches nothing well. Document structure usually gives better boundaries than a fixed token count, and fixed-size chunking is the default that most systems never revisit.

What an assigned team does with RAG.

RAG systems need evaluation on retrieval separately from generation. Measuring whether the right documents were retrieved, independently of what the model then said, is what makes the system improvable rather than a black box people argue about.

Building that evaluation harness before scaling the corpus is the sequence that works, and it is assigned as explicit scope under ai engineering services.

What we use RAG for.

• Answers grounded in your corpus Responses drawn from your documents rather than from model training data.
• Chunking that follows document structure Boundaries at sections rather than token counts, so passages stay meaningful.
• Retrieval measured separately Recall evaluated on its own, because that is where most failures originate.

How RAG capacity is assigned.

RAG capacity is assigned with retrieval evaluation as a first-class deliverable, because a system nobody measures cannot be improved deliberately.