A list of insane prompting techniques that will genuinely change how you use AI.

COREATI

(just tell your Claude agent to teach you these):

• Chain-of-thought (COT) prompting

• Socratic prompting

• Reverse-prompting

• Drip-feed context management

• Interview prompting

• Role + Task + Constraint (simple prompting style)

• JSON/XML tags

Just implementing a few of these will dramatically change your AI responses.

COREATI

[CHAIN-OF-THOUGHT PROMPT]

Solve the problem step by step.

Explain your reasoning clearly before giving the final answer.

Problem:

COREATI

[SOCRATIC PROMPT]

Ask me questions one by one until you fully understand the problem.

Do not answer until you are confident you have all necessary information.

Then provide the final answer.

Problem:

COREATI

[REVERSE PROMPTING]

Write the most effective prompt possible to solve the following problem.

Optimize for clarity, depth, and accuracy.

Problem:

COREATI

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COREATI

[DRIP-FEED CONTEXT – STEP 1]

Understand the goal only.

Do not solve yet.

Goal:

[DRIP-FEED CONTEXT – STEP 2]

Now consider these constraints.

Do not solve yet.

Constraints:

[DRIP-FEED CONTEXT – STEP 3]

Now use all previous context and solve.

Data:

COREATI

[INTERVIEW PROMPTING]

Act as an expert interviewer.

Ask me targeted questions one at a time to extract all necessary details.

Once complete, produce the best possible solution.

Topic:

COREATI

[ROLE + TASK + CONSTRAINT]

You are:

Your task:

Constraints:

Output:

Give a clear, structured answer.

COREATI

[JSON STRUCTURED OUTPUT]

Return the answer in JSON format.

Schema:

{

“summary”: "",

“steps”: [],

“risks”: [],

“final_answer”: ""

}

Problem:

COREATI

[XML STRUCTURED OUTPUT]

Return the answer in XML format.

Structure:

<final_answer></final_answer>

</response>

Problem:

COREATI

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