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#fine-tuning

2 articles — updated daily

Fine-Tuning vs Prompt Engineering vs RAG: A Builder's Guide
LLMs

Fine-Tuning vs Prompt Engineering vs RAG: A Builder's Guide

Fine-tuning, prompt engineering, and RAG each solve a different failure mode, and picking the wrong one is an expensive way to paper over a problem you never diagnosed. OpenAI's May 2026 wind-down of its self-serve fine-tuning platform, now down to a single model, and Anthropic's decision never to ship a public fine-tuning API both signal a market retreat from an over-used technique. The bigger signal: most 'let's fine-tune it' requests turn out to be knowledge gaps or instruction gaps in disguise, fixable with retrieval or a better prompt at a fraction of the cost. For PMs, the takeaway is a triage habit: diagnose whether you're facing stale facts, inconsistent format, or a genuinely novel reasoning task before you authorize a training run.

6 min read →
Catastrophic Forgetting: Why Fine-Tuning Can Make Models Dumber
LLMs

Catastrophic Forgetting: Why Fine-Tuning Can Make Models Dumber

Catastrophic forgetting is the tendency for fine-tuning to sharpen a language model on a narrow task while quietly degrading capabilities it never touched during training. It happens because fine-tuning overwrites shared weights rather than adding isolated new knowledge, and the severity ranges from negligible to severe depending on method, learning rate, and how repetitive the data is. As fine-tuning becomes a default lever for teams shipping specialized AI features, the gap between "it got better at X" and "it got worse at everything else" is turning into a quiet source of production incidents. For PMs, the fix isn't avoiding fine-tuning, it's building an eval suite that checks capabilities you didn't train for, not just the one you did.

6 min read →
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