Expert-Level Prompting
Master-level prompting techniques for highest precision and performance
These advanced techniques are intended for experienced users who want to get the maximum out of AI systems.
Constitutional AI Prompting
Self-Critique and Improvement
Task: [Your request]
After formulating an initial response:
1. Evaluate your answer according to these criteria:
- Factual accuracy
- Completeness
- Clarity and comprehensibility
- Practical applicability
2. Identify areas for improvement
3. Create a revised, optimized version
4. Present only the final, improved answerEthics Framework Integration
Before answering, check:
- Fairness: Is the answer balanced and unbiased?
- Harm: Could the information be misused?
- Transparency: Are uncertainties clearly marked?
- Benefit: Does the answer maximize value for the user?
Adjust your response accordingly.Recursive Prompting
Self-Expanding Prompts
Step 1: Analyze this request and identify 3 sub-questions
that need to be answered.
Step 2: For each sub-question:
- Formulate it precisely
- Answer it thoroughly
- Identify other relevant aspects
Step 3: Synthesize all partial answers into a coherent
overall response.
Step 4: If important new questions have emerged, repeat
the process once.Depth-First Exploration
Topic: [Your topic]
1. Choose the most important aspect
2. Explore it to complete depth
3. Document all insights
4. Return and choose the next aspect
5. Repeat until all aspects are covered
6. Create an integrated summaryAdversarial Prompting
Counter-Argumentation
Thesis: [Your position]
1. Formulate the strongest argument FOR this thesis
2. Switch perspective: Formulate the strongest COUNTER-arguments
3. Analyze both sides objectively
4. Identify synthesis opportunities
5. Present a nuanced conclusionStress Testing
Proposed solution: [Your idea]
Test this solution against:
1. Edge cases
2. Scaling problems
3. Unforeseen side effects
4. Resource constraints
5. Human factors
For each identified problem: Suggest adjustmentsPrompt Programming
Functional Prompts
DEFINE FUNCTION analyze_text(text, parameters):
aspects = extract_aspects(parameters)
results = {}
FOR EACH aspect IN aspects:
results[aspect] = deep_analyze(text, aspect)
RETURN synthesize(results)
END FUNCTION
# Execution
analyze_text(
text="[Your text]",
parameters={
"aspects": ["sentiment", "key_points", "implications"],
"depth": "comprehensive",
"output_format": "structured"
}
)Conditional Logic
IF input_type == "technical":
use_technical_language = True
detail_level = "high"
ELSE IF input_type == "general":
use_technical_language = False
detail_level = "medium"
add_examples = True
ELSE:
ask_for_clarification()
Generate_response_with_parameters()Emergent Behavior Prompting
Creativity Maximization
Think outside all conventional boundaries:
1. Initially ignore all practical constraints
2. Combine concepts from completely different domains
3. Question fundamental assumptions
4. Develop 5 radically innovative approaches
5. Only then: Adapt the most promising for realitySynergy Discovery
Elements: [A, B, C]
Examine:
1. A + B: What synergies emerge?
2. B + C: What new possibilities?
3. A + C: What unexpected connections?
4. A + B + C: What emergent properties?
5. Identify the synergy maximumMulti-Modal Reasoning
Cross-Domain Transfer
Problem in Domain A: [Description]
1. Abstract the core problem
2. Find analogous problems in Domains B, C, D
3. How were they solved there?
4. Transfer solution principles back to Domain A
5. Adapt for specific contextMetaphorical Thinking
Complex concept: [Your concept]
1. Find metaphors from:
- Nature (biological systems)
- Technology (machines, software)
- Society (social structures)
- Art (music, painting)
2. For each metaphor:
- What are the parallels?
- What insights does it offer?
- Where are the limits?
3. Synthesize a new, powerful explanationAdvanced Output Control
Probabilistic Thinking
Question: [Your question]
Answer structure:
1. Most likely answer (70-90% confidence)
2. Alternative scenarios (20-30% confidence)
3. Unlikely but possible cases (5-10%)
4. Black Swan events (<1% but high impact)
For each scenario: Justification and implicationsGranular Control
Parameters for this response:
- Technical level: [1-10]
- Example density: [low/medium/high]
- Cultural context: [specify]
- Time horizon: [short/medium/long-term]
- Risk tolerance: [conservative/balanced/progressive]
Generate tailored response based on these parameters.Meta-Learning Prompts
Prompt Optimization
Original prompt: [Your prompt]
1. Analyze strengths and weaknesses
2. Generate 3 improved versions:
- Version A: Clearer structure
- Version B: More precise requirements
- Version C: Better examples
3. Test each version
4. Combine the best elements
5. Present optimal promptAdaptive Prompting
After each response evaluate:
- Was the answer too detailed/superficial?
- Was the desired style achieved?
- Were there misunderstandings?
Adjust the next prompt accordingly:
- For too little detail: "Explain in more detail..."
- For wrong style: "In the style of..."
- For misunderstandings: "To clarify..."Advanced Applications
Complex Decision Making
Decision problem: [Description]
Framework:
1. Stakeholder analysis (Who is affected?)
2. Criteria definition (What is important?)
3. Option generation (What is possible?)
4. Impact analysis (What are the consequences?)
5. Risk assessment (What can go wrong?)
6. Synergy check (How to combine?)
7. Temporal consideration (Short vs. long term)
8. Decision matrix
9. Sensitivity analysis
10. Final recommendation with confidence levelInnovation Framework
Innovation goal: [Your goal]
Phase 1 - Divergence:
- Brainstorming without limits
- Cross-industry inspiration
- Future scenarios
- Wild combinations
Phase 2 - Convergence:
- Feasibility assessment
- Resource analysis
- Market potential
- Implementation path
Phase 3 - Synthesis:
- Top 3 innovation concepts
- Detailed elaboration
- Prototyping plan
- Success metricsAchieving Mastery
True mastery in prompting comes through:
- Continuous experimentation - Try new techniques
- Systematic learning - Document successes and failures
- Creative combination - Mix techniques situationally
- Deep understanding - Understand AI functionality
- Ethical reflection - Responsible use