prompt-craft

To fix a prompt not following instructions, you can analyze its failures and rewrite it using proven prompt engineering techniques like positive framing, structured output, and placement adjustments to improve model guidance and instruction following.

The best way to build a structured output prompt is to use a crafting mode that applies research-backed techniques like verbalized sampling and reasoning-first design, ensuring the model produces reliable and correctly formatted responses.

Your prompt underperforms across different models because generic prompts lack model-aware adjustments. Diagnosing the prompt and applying model-specific tuning avoids incorrectly configured guidance and yields higher quality output.

Yes, you can analyze an existing prompt by scoring it against a core set of prompt engineering techniques such as role definition, salience, and self-reflection to identify exactly why it fails and how to optimize it.

Prompt engineering techniques that help with reasoning and formatting tasks include reasoning-first structuring, structured output formatting, verbalized sampling, and self-reflection to guide the model through complex instructions.

For a quick prompt fix, target model specifications are optional but recommended. Providing the target model allows the rewriting process to tailor the optimized replacement prompt specifically to that model's guidance requirements.