RAG Is Not a Product Strategy
RAG is a powerful technique, but it's being wildly overapplied. Most 'we need RAG' conversations should start with 'do we actually need AI for this?'
Retrieval-Augmented Generation has become the hammer that makes everything look like a nail. Every company with a document library thinks they need a RAG pipeline. Most of them need better search, or a well-organized wiki, or someone to actually read the documentation they already have.
When RAG Makes Sense
RAG works well in a narrow set of conditions: you have a large, frequently updated corpus of information, users need to synthesize across multiple documents, and the questions are complex enough that keyword search genuinely fails.
When It Doesn’t
If your documents are relatively static and well-organized, you probably need better navigation, not AI. If people are asking simple factual questions, a good search engine with snippets will do. If accuracy is critical (medical, legal, financial), RAG’s tendency to hallucinate is a liability, not a feature.
The Real Question
Before building a RAG system, ask: “If we had perfect search, would that solve 80% of the problem?” If yes, build better search. It’s faster, cheaper, more reliable, and easier to maintain.
Practical AI strategy and implementation guidance for business leaders. No hype, no fluff — just what works.
Why Your AI Pilot Won't Scale (And What to Do About It)
Pilots succeed in controlled conditions. Production requires dealing with everything you conveniently excluded from the pilot.