OpenAI o3-mini Prompt Engineering: Controlling Reasoning Effort Parameters
Guide to tuning reasoning_effort ('low', 'medium', 'high') in OpenAI API calls for optimal latency and cost.
Topic
#Prompt Engineering
2 articles — updated daily
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.