Summary
- Reasoning models are trained to mimic humanlike problem-solving and analytical thinking.
- Their performance and usefulness are influenced by the quality of the prompts that are given to them.
- Poorly constructed or unclear prompts can derail their performance.
- Here are some best practices for crafting optimal prompts to get the best from your reasoning models:
- Give clear and specific instructions to help guide the model’s responses, being sure not to miss out any important context or information.
- Structure your prompt like a question to provide a clear objective for the model to focus its response on.
- Make sure not to give the model incomplete or ambiguous information.
- Be careful not to overload the model by feeding it too much data at once.
- Provide clear boundaries and constraints to help the model stay focused on specific objectives.
- Include a call to action to nudge the model to provide actionable and practical outcomes.
- Give the model the freedom to dream up creative solutions and responses while making sure it stays within the realms of possibility.
- Guide the model towards providing well-supported responses using logic and reasoning.
By Tyler Fyock
Original Article