What is prompt engineering for RAG?

Prompt engineering for RAG is writing the instructions that force the model to answer only from the chunks you just retrieved, instead of falling back on whatever it memorized during training.

Retrieval finds the right paragraphs; the prompt is what makes the model actually use them instead of guessing. Skip that step and the model happily blends facts from your documents with facts it half-remembers from training, and there's no way for a reader to tell which is which. That's how a support bot ends up confidently quoting a pricing plan you retired two years ago.

The practices that actually matter

• Say it outright: Answer using only the context below. If the answer isn't there, say you don't have that information instead of guessing. This single line is the difference between a grounded answer and a confident guess.
• Label each retrieved chunk with its source, like [Source: refund-policy.pdf] before the chunk text. If every chunk just runs together in one block, the model can't tell where one document ends and another begins, and neither can you when something goes wrong.
• Ask the model to cite its sources in the answer itself, so a reader can check a claim against the original document instead of taking it on faith.
• Put these instructions before the chunks, not buried after a long wall of retrieved text, since models pay less attention to anything stuck in the middle of a long prompt.

None of this is complicated, but skipping it is exactly why some RAG bots quietly make things up while sounding completely sure of themselves. Getting the prompt right is what makes a RAG system something you can actually trust.