Complex Tasks and Reasoning
Prompting Reasoning Models
Prompt reasoning models by defining outcomes, constraints, and success criteria while avoiding unnecessary prescription of the reasoning path.
In this lesson
Why this matters
Reasoning models can benefit from direct instructions and platform-specific controls. Focus on goals, constraints, and evidence, and request concise checkable justification rather than hidden chain-of-thought.
How to apply it
- Define the task and desired outcome.
- Add only context or rules that affect success.
- Specify an output form or checkable criterion when needed.
- Test on realistic input and revise when a failure pattern appears.
Practical example
Weak example:
Weak version of the same task: اكشف كل تفكيرك الداخلي خطوة بخطوة.The weak version leaves an important part of the task or its boundaries to guesswork. The improved version adds information tied directly to success without treating length or complexity as a goal.
Improved example:
Improved version: قارن الخيارين حسب التكلفة والمخاطر والزمن، ثم أعط النتيجة مع 3 أسباب قابلة للتحقق وأي افتراضات.Short practice
Apply the lesson concept to this task: حوّل طلب «فكر خطوة بخطوة» إلى معايير قرار وتبرير موجز.
Check your understanding
Check: ما البديل لطلب سلسلة التفكير؟
Answer: طلب نتيجة مع أسباب أو أدلة قابلة للتحقق دون كشف التفكير الداخلي.
Summary
Keep this concept as one tool rather than a magic formula. Tie it to the goal and success criteria, then build on it in the next lesson.