Fine-tuning vs. RAG vs. prompt engineering: how are you choosing in 2026?
The tradeoffs keep shifting as base models improve. What's your current decision framework, and when did you last revisit a fine-tuned model and decide prompting was good enough?
8 replies
Interesting framing. The bottleneck in our case wasn't where we assumed. Worth a short experiment before committing to a solution.
The hard part is governance: speed without guardrails becomes compounding debt within a quarter.
The metric that moved most wasn't the one we were watching. We only noticed in the quarterly retrospective.
Documentation and worked examples mattered more than tooling for us — especially when adoption was uneven across the team.
Context matters enormously here. What worked at 5 engineers was a liability at 25.
This matches what we saw. The part that surprised us was how long it took for the team to trust the new approach.
The cultural piece is underrated. Technical solutions are fast; getting a team to consistently use them takes much longer.
Worth being explicit about assumptions before starting — we wasted two weeks discovering constraints that were knowable upfront.
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