RAG-or-Fine-Tuning Finder
Which approach fits: RAG, fine-tuning, prompting, or long context?
"We want our own AI model" is one of the most common sentences in first conversations – but it almost always means: the AI should know and use our own company knowledge. There are four fundamentally different ways to do that, and none is universally the best. Answer 7 short questions about your case and get a reasoned recommendation – with the honest limits of each approach, not just the upsides.
The RAG-or-Fine-Tuning Finder uses seven questions about scope, freshness, traceability, data protection, and query patterns to recommend which of the four common approaches – prompting, RAG, long context, or fine-tuning – best fits letting an AI work with your company knowledge.
Your case
Your recommendation
Fit
Recommended: RAG (Retrieval-Augmented Generation)
The AI searches your documents in a targeted way on every request and only gets the matching excerpts – without being trained.
- Stays cost-efficient even with large, growing knowledge bases
- New knowledge is usable without retraining
- Can cite sources – an important advantage for traceability
- Only the relevant excerpts go to the AI, not the entire knowledge base
Building it requires real engineering (preparation, search, infrastructure) – not a five-minute project. And quality lives or dies with the search: if it finds the wrong excerpts, the answer will be wrong too.
Also relevant: Long context (long-context window)
The entire document collection is packed directly into a very large context window on every request – without targeted search.
Close to the recommendation – a hybrid approach combining both often pays off (e.g. targeted retrieval plus a bit more context).
Not sure which approach fits your project?
How this finder scores
- Seven questions, each mapped to one of seven decision dimensions (effort, scope, freshness, traceability, knowledge vs. style, data protection, query volume) – derived from research into all four approaches, including Anthropic's engineering blog, the Stanford FineTuneBench study, and the original RAG paper (Lewis et al., 2020).
- Each answer awards points to the approaches demonstrably best suited to that situation. The evaluation runs deterministically in your browser, with no AI and no inputs sent anywhere.
- Fit scores should be read relative to each other (0-100% per approach), not as an absolute guarantee of success.
- Deliberately honest: even the recommended approach shows its documented downside – no approach comes without a trade-off.
This finder provides well-founded orientation, not a technical specification. The actual best solution depends on details best clarified in conversation – and it's often a combination of several approaches anyway.
Frequently asked questions about the RAG-or-Fine-Tuning Finder
RAG or fine-tuning – which is the better choice in 2026 for teaching an AI company knowledge?
RAG, in most cases: a Stanford study (FineTuneBench) found fine-tuning achieves only 37% accuracy for learning new knowledge and 19% for updating it – markedly worse than RAG's grounding in real documents. On top of that, OpenAI is winding down its public fine-tuning platform entirely by early 2027. Fine-tuning remains useful for a fixed style or format at very high, narrow query volume – but not for knowledge.
Isn't a prompt with our key info enough?
Yes, for a small, stable amount of knowledge (e.g. a short FAQ). But accuracy demonstrably declines well before the context window's technical limit ("context rot") – Anthropic itself recommends targeted retrieval over pre-loaded context past a certain size.
Why not just use a model with a huge context window and paste everything in?
Technically possible, but without caching it's often notably more expensive per request than RAG, and reliability demonstrably drops for very long contexts packed with multiple relevant facts ("lost in the middle"). It also sends complete raw documents to the AI provider instead of just relevant excerpts.
Is fine-tuning still worth it in 2026?
Practically not for knowledge transfer anymore – see above. For a very specific, reliable style or format at extremely high, uniform volume it can still be worthwhile, though availability at the major providers is now sharply limited in 2026 (OpenAI is winding it down, Google's public API has not offered it since 2025).
Do the four approaches rule each other out?
No. Mature systems increasingly combine multiple approaches, such as targeted retrieval (RAG) followed by processing the found passages in a larger context. That's why the finder also shows a second, close option when your answers land nearly between two approaches.