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Retrieval-augmented generation is sold as the cure for hallucination. Stanford measured three RAG-based legal tools and found they fabricated answers 17% to 33% of the time. Here is the actual pipeline, the four places it breaks, and why almost all RAG debugging is search debugging.
The line that clicked for me: behavior belongs in the weights, knowledge belongs in the context. Stop fine-tuning your FAQ into a model. The job that actually moves the needle now is curating the smallest set of high-signal tokens the model sees at inference time.
'AI engineer' isn't machine learning wearing a new hat. It's a different job that starts from a product and reaches for a model, not the other way around. Most job posts are confused about which one they're hiring for, and that confusion is where your leverage is.
