Meta Prompting

---
provider: OpenAI
model: gpt-4o
temperature: 0.6
---

<step as="initial_prompt">
# Initial Meta Prompt Generation

I need to accomplish the following task:

{{ task_description }}

Generate a prompt that would help an AI complete this task effectively. Consider:
- The specific goal to be achieved
- The expected format of the output
- Any constraints that must be followed
- The target audience for the result
- The level of detail required

## Generated Prompt:
</step>

<step as="prompt_critique">
# Meta Prompt Critique

Analyze the following prompt that was created to accomplish this task:

## Task:
{{ task_description }}

## Generated Prompt:
{{ initial_prompt }}

Now critique this prompt based on the following criteria:
1. **Clarity**: Is the prompt clear and unambiguous?
2. **Specificity**: Does it provide enough guidance on what exactly is needed?
3. **Constraints**: Does it properly communicate any limitations or requirements?
4. **Example Guidance**: Could examples help make the expectations clearer?
5. **Structure**: Is the prompt structured in a way that guides the AI's response format?

## Prompt Critique:
Identify at least 3 specific ways this prompt could be improved.
</step>

<step>
# Final Optimized Prompt

Based on the initial prompt and critique, create an improved, optimized prompt:

## Original Task:
{{ task_description }}

## Initial Prompt:
{{ initial_prompt }}

## Critique Points:
{{ prompt_critique }}

## Optimized Final Prompt:
Create a new and improved version of the prompt that addresses the critique points while maintaining the core objectives of the task.
</step>

---
provider: OpenAI
model: gpt-4o
temperature: 0.8
---

<step as="prompt_variants">
# Generate Prompt Variants

For the following task, generate 3 different prompting approaches, each with a different strategy:

## Task:
{{ task_description }}

## Prompt Variant 1 - Direct Instruction Style:
Create a prompt that uses clear, direct instructions with step-by-step guidance.

## Prompt Variant 2 - Role-Based Style:
Create a prompt that assigns a specific expert role to the AI and frames the request within that context.

## Prompt Variant 3 - Example-Based Style:
Create a prompt that uses examples (few-shot learning) to demonstrate the expected output format and quality.
</step>

<step as="prompt_evaluation">
# Evaluate Prompt Variants

Analyze these three prompt variants for the task:

## Task:
{{ task_description }}

## Prompt Variants:
{{ prompt_variants }}

For each prompt variant, evaluate:
1. **Likely Effectiveness**: How well will it guide the AI to complete the task?
2. **Clarity**: How clear are the instructions?
3. **Adaptability**: How well would it handle edge cases?
4. **Efficiency**: How concise is the prompt without sacrificing effectiveness?

Score each prompt on a scale of 1-10 for each criterion, and provide a brief explanation.

## Evaluation Results:
</step>

<step>
# Select Optimal Prompt

Based on the evaluation, select the best prompt variant or create a hybrid that combines the strengths of multiple variants.

## Evaluation Summary:
{{ prompt_evaluation }}

## Optimal Prompt Selection:
The optimal prompt for this task is:

[Provide the selected or hybrid prompt here]

## Reasoning:
[Explain why this prompt is the best choice for the specific task]
</step>

---
provider: OpenAI
model: gpt-4o
temperature: 0.5
---

<step as="task_analysis">
# Task Decomposition

Analyze the following complex task and break it down into sequential sub-tasks:

{{ complex_task }}

## Task Analysis:
1. Identify the main goal
2. Determine required components
3. Map out a logical sequence of steps

## Task Decomposition Results:
List the sequence of sub-tasks needed to complete this complex task.
</step>

<step as="prompt_generation">
# Sub-Task Prompt Generation

Based on the task decomposition, create optimized prompts for each sub-task:

## Complex Task:
{{ complex_task }}

## Decomposed Sub-Tasks:
{{ task_analysis }}

## Generated Sub-Task Prompts:
For each sub-task, create a specialized prompt designed to accomplish that specific part of the overall process.
</step>

<step>
# Meta Workflow Assembly

Assemble the sub-task prompts into a cohesive workflow that addresses the complex task:

## Original Complex Task:
{{ complex_task }}

## Sub-Task Prompts:
{{ prompt_generation }}

## Workflow Integration:
Create a step-by-step workflow that:
1. Structures the sub-tasks in the optimal sequence
2. Ensures output from earlier steps feeds correctly into later steps
3. Includes validation checks between stages
4. Provides a final integration step to combine results

## Complete Meta Prompt Workflow:
</step>

---
provider: OpenAI
model: gpt-4o
temperature: 0.6
---

<step as="initial_result">
# Initial Task Execution

## Task:
{{ task_description }}

## Initial Prompt:
{{ initial_prompt }}

Execute the task using the provided prompt and produce a result.

## Initial Result:
</step>

<step as="error_analysis">
# Performance Analysis

Analyze the initial result against the expected outcome:

## Task:
{{ task_description }}

## Expected Outcome:
{{ expected_outcome }}

## Initial Result:
{{ initial_result }}

## Performance Gap Analysis:
1. Identify specific areas where the result falls short
2. Analyze why the initial prompt led to these shortcomings
3. Determine what prompt elements could be adjusted to improve results

## Analysis Results:
</step>

<step>
# Prompt Adaptation

Based on the performance analysis, adapt the prompt to address identified issues:

## Original Prompt:
{{ initial_prompt }}

## Performance Issues:
{{ error_analysis }}

## Adapted Prompt:
Create an improved prompt that specifically addresses the identified performance gaps while maintaining the original task objectives.
</step>

---
provider: OpenAI
model: gpt-4o
temperature: 0.5
---

# Automated Prompt Engineer

I'll help you create an optimized prompt for your specific use case.

## Use Case Information:
- Task description: {{ task_description }}
- Target audience: {{ audience }}
- Desired output format: {{ output_format }}
- Important constraints: {{ constraints }}
- Key success criteria: {{ success_criteria }}

## Prompt Engineering Process:
1. **Task Analysis**:
   - Core objectives of the task
   - Knowledge domains required
   - Potential challenges or ambiguities

2. **Audience Adaptation**:
   - Adjusting complexity level for {{ audience }}
   - Addressing specific needs or concerns

3. **Structure Design**:
   - Format that guides the desired output
   - System, user, and assistant message allocation
   - Appropriate examples or demonstrations

4. **Constraint Integration**:
   - How to enforce {{ constraints }} effectively
   - Error prevention strategies

## Engineered Prompt:
[Generated optimal prompt]

## Usage Instructions:
- How to implement this prompt
- Variables to customize
- Expected performance characteristics

---
provider: OpenAI
model: gpt-4o
temperature: 0.4
---

<step as="content_analysis">
# Content Analysis

Analyze the following content for optimization opportunities:

{{ original_content }}

## Content Assessment:
1. **Purpose**: What is this content trying to achieve?
2. **Audience**: Who is the target audience?
3. **Structure**: How is the information organized?
4. **Tone**: What is the current tone and style?
5. **Weaknesses**: What aspects could be improved?

## Analysis Results:
</step>

<step as="optimization_prompt">
# Optimization Prompt Generation

Based on the content analysis, create a prompt specifically designed to optimize this content:

## Content Analysis:
{{ content_analysis }}

## Optimization Goals:
- Maintain the original purpose and key information
- Address identified weaknesses
- Enhance clarity and engagement
- Optimize for the target audience
- Improve structure and flow

## Content Optimization Prompt:
Create a prompt that would guide an AI to transform the original content into an optimized version.
</step>

<step>
# Content Transformation

Using the optimization prompt, transform the original content:

## Original Content:
{{ original_content }}

## Optimization Prompt:
{{ optimization_prompt }}

## Optimized Content:
Transform the original content according to the optimization prompt.
</step>