TL;DR: Stop settling for vague AI suggestions. This structured XML prompt forces the model to act as a rigorous analyst, delivering specific action plans and identifying pitfalls instead of generic encouragement.
We have all been there. You type out a complicated situation involving office politics or a difficult project, hoping for a strategy, and the AI responds with “maintain open communication” or “stay positive.” It is frustrating because you need a roadmap, not a fortune cookie. The original poster, u/Distinct_Track_5495, faced this exact issue and decided to engineer a solution that forces the AI to get specific.
Instead of casually asking for advice, this Redditor built a strict framework using XML tags. This approach creates a “container” for the AI’s behavior, preventing it from drifting into generalities. I found this particularly clever because it treats the prompt like code rather than conversation, which often yields much higher adherence to instructions.
The Analyst & Action Planner Prompt
Here is the exact prompt provided by the author. You will need to replace the placeholders at the bottom with your specific details.
<prompt>
<persona>
<role>Expert Scenario Analyst & Action Planner</role>
<goal>To break down a complex problem into a clear, step-by-step action plan tailored to the user’s specific situation.</goal>
<constraints>
<constraint>Each step must be actionable and specific.</constraint>
<constraint>Each step must directly address an element of the user’s provided scenario.</constraint>
<constraint>Avoid generic advice or platitudes.</constraint>
<constraint>Focus on immediate, achievable actions first, then progress to longer-term strategies.</constraint>
<constraint>Include potential pitfalls or considerations for each step where relevant.</constraint>
<constraint>Structure the output as a numbered list.</constraint>
</constraints>
<tone>Pragmatic, direct, and encouraging.</tone>
</persona>
<context>
<user_scenario>
USER_PROVIDED_SCENARIO_HERE
</user_scenario>
<desired_outcome>
USER_PROVIDED_DESIRED_OUTCOME_HERE (optional, but helpful)
</desired_outcome>
</context>
<instruction>
Analyze the provided user scenario and desired outcome. Based on this analysis, generate a detailed, step-by-step action plan to address the scenario and achieve the desired outcome. Ensure every step is concrete, specific, and directly applicable to the situation described. For each step, briefly mention any potential challenges or considerations.
</instruction>
</prompt>
Why This Structure Delivers Results
The effectiveness of this prompt lies in its architectural rigidity. The author uses a few key techniques that prevent the Large Language Model (LLM) from taking the path of least resistance.
- XML Tagging (The Skeleton): By wrapping sections in tags like <role>, <constraints>, and <instruction>, the author gives the AI a clear hierarchy of information. LLMs are trained on vast amounts of code, including HTML and XML. When they see this structure, they understand that <constraints> are rules that apply globally to the output, not just text to be read.
- Explicit Negative Constraints: Notice the constraint: “Avoid generic advice or platitudes.” This is crucial. Without this instruction, models tend to default to safe, agreeable, and ultimately useless advice. By explicitly banning “fluff,” the author forces the model to dig deeper into its training data for concrete solutions.
- The “Pitfall” Requirement: The instruction to “Include potential pitfalls or considerations for each step” transforms the output from a wish list into a strategic plan. It forces the AI to simulate the execution of the advice and predict failure points, which adds significant value to the response.
🛠️ Ways to Customize This Prompt
While the original post offers a solid foundation, you can tweak the parameters to fit different needs. Here are two variations I think would add even more utility.
- Add an Output Format: You could modify the <instruction> section to request a table. For example: “Output the plan as a Markdown table with columns for ‘Action Step,’ ‘Expected Timeframe,’ and ‘Risk Level.'” This makes the advice easier to scan and schedule.
- The Devil’s Advocate: Add a second step to the instruction. Ask the AI to assume the role of a skeptic after generating the plan and critique its own advice. This “Chain of Thought” prompting can help identify weaknesses in the plan before you even try to implement it.
This prompt is a great example of how a little bit of structure can completely change the quality of AI interactions. If you are tired of polite but useless suggestions, give this XML method a shot!
Check out the full discussion on Reddit for more context.
Fed up with generic AI “expert” advice? Try my Analyst & Action Planner
by u/Distinct_Track_5495 in ChatGPTPromptGenius