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1. AI-Guided Iterations

Definition:

A method where the AI iteratively reviews, revises, and refines its own prompts, answers, or reasoning — often autonomously — to improve performance or quality.

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Core Techniques:

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A. Self-Refinement Loop

The AI is asked to critique and improve its own output.

Example:

Prompt: Generate a product description for a luxury smartwatch.

AI: [writes a draft]

Follow-up: Critique and revise the previous output for tone, clarity, and persuasion.

Repeat this loop until satisfactory.

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B. Chain-of-Thought (CoT) + Iterative Verification

Add CoT reasoning, then use another prompt to validate logic.

Example:

Q: How many legs do 3 dogs and 2 spiders have? A1: Let's think step by step...

Then: Q2: Is this answer correct? If not, fix it.

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C. Prompt Scoring and Selection

Use the AI (or a second AI) to evaluate multiple prompt variants and choose the best.

Structure:

Prompt A → Result A Prompt B → Result B

Evaluator Prompt: Compare A and B and select the better one.

Used in AutoPrompt and Reflexion-style agents.

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2. Prompt Stacking

Combine multiple optimized prompts with transition logic. Often used in multi-agent systems or tool-using agents.

Example:

Prompt 1: Extract key entities from this text. Prompt 2: Classify the tone of the message. Prompt 3: Suggest a next action based on tone + entities.

Useful in:

• Customer support automation
• LLM chains
• Decision-making pipelines </aside>

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3. Prompt Rewriting via AI ("Reflexive Prompting")

Let the model rewrite its own prompt for clarity, brevity, or task alignment.

Prompt: Rewrite the following prompt to be more concise but retain all critical instructions.

Input: "As a customer service agent, respond in a warm and respectful tone to a customer who is upset..."

The rewritten prompt is used downstream. Useful for prompt compression or fine-tuning workflows.

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4. Few-Shot Bootstrapping with Self-Generated Examples

Let the model generate its own few-shot examples to prime itself.

Step-by-step:

1. Give a high-level task description.
2. Ask AI to generate 3 examples.
3. Use those as part of the prompt for future runs.

Prompt:

Describe a task. Then generate 3 input-output examples.

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5. Meta-Prompts (Prompting About Prompts)

Use prompts to instruct the model on how to write or improve prompts.

Example:

Instruction: You're an expert in prompt engineering. Create a prompt to extract legal obligations from a contract.

This creates scalable prompt design tools. Very useful in:

• Agents that design prompts
• LLM-as-a-service platforms </aside>

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6. Latent Prompt Evolution (Inspired by Evolutionary Algorithms)

1. Generate N prompts.
2. Score them based on a metric (accuracy, coherence, etc.).
3. Mutate top performers to create new generations.
4. Repeat.

Tools that support this:

• PromptTools
• AutoGPT, GPT Engineer (for iterative builds)
• OpenAI’s Evals framework (for benchmarking) </aside>

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7. Reflection & Retrospection (Used in agents like ReAct, Reflexion)

The model is prompted to reflect on past errors or outputs to improve future performance.

Example:

After a failed reasoning step, prompt: "What went wrong in the previous reasoning? How can we avoid that mistake?"

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8. Synthetic Data Generation for Prompt Tuning

Use AI to generate high-quality training examples to refine or test prompts at scale.

Prompt: Generate 20 realistic support tickets from e-commerce users and label them by urgency and topic.

Use these examples in:

• Few-shot prompts
• Finetuning
• Evaluation datasets </aside>

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Advanced Prompt Techniques