Detailed prompts unlock local LLM performance

The "zero-shot" trap is real—many developers are testing their own poor prompting rather than the model's actual reasoning limits.

• –System prompts are more than just personas; they must map out the environment and tool API for the model to succeed.
• –Explicitly granting permission to experiment with tools reduces model "stalling" when a single path fails.
• –Benchmarks using bare prompts unfairly favor massive closed models over well-prompted local ones.
• –Shifting from "AI" to "LLM" terminology helps manage expectations regarding the model's inherent context gaps.