Plan-and-Solve prompting instructs the AI to first devise a step-by-step plan, then execute it. The separation prevents the model from rushing to an answer before thinking through the approach. It consistently outperforms direct prompting on multi-step analysis, research tasks, and complex problem-solving.
Plan-and-Solve prompting was introduced as an improvement over standard Chain-of-Thought prompting. The key difference is the explicit two-phase structure: Phase 1 generates a plan (what steps to take), Phase 2 executes that plan (doing the actual work). This separation reduces errors caused by the AI skipping steps or losing track of the overall approach midway through execution.
Use Plan-and-Solve for research tasks, competitive analysis, structured reports, debugging sessions, and any multi-step problem where jumping straight to an answer produces shallow results. It is especially effective when the task requires gathering or considering multiple pieces of information in a specific order. For simpler tasks, use RTF or RACE.
Plan-and-Solve vs Chain-of-Thought: CoT asks the AI to show its reasoning; Plan-and-Solve explicitly separates planning from execution, which reduces step-skipping on complex tasks. Plan-and-Solve vs RISEN: RISEN gives the AI a fixed sequence of steps to follow; Plan-and-Solve asks the AI to generate its own plan first. Plan-and-Solve vs Skeleton-of-Thought: Skeleton-of-Thought outlines content structure; Plan-and-Solve plans a problem-solving process.
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Start freePlan-and-Solve prompting instructs the AI to first create a step-by-step plan for a task, then execute that plan. The two-phase approach prevents the model from rushing to an answer before thinking through the approach, which improves accuracy and completeness on multi-step tasks.
Chain-of-Thought asks the AI to show its reasoning as it works toward an answer. Plan-and-Solve explicitly splits the process into two phases: generate a plan first, then execute it. The explicit planning phase reduces step-skipping and produces more thorough results on complex analysis tasks.
Use Plan-and-Solve when the task has multiple distinct phases, when rushing to an answer without a plan produces incomplete results, or when you want the AI to cover a topic comprehensively before delivering the final output. It works especially well for research, analysis, and debugging tasks.
Yes. The technique is model-agnostic and works across all major language models. Models with stronger instruction-following tend to adhere to the plan-first constraint more reliably.