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Meta-Prompting: The Art of Prompt Engineering

Context: This file contains advanced techniques to get the absolute best out of Large Language Models (LLMs).

🧠 Core Techniques​

1. Chain of Thought (CoT)​

Theory: Forcing the model to "show its work" improves reasoning capabilities for complex problems. Prompt: "Let's think step by step. First, analyze the constraints. Second, propose three possible solutions. Third, evaluate the trade-offs of each. Finally, pick the best one."

2. Few-Shot Prompting​

Theory: Providing examples establishes a pattern for the model to follow. Prompt: "Convert these sentences into JSON objects: Input: 'John is 30 years old.' Output: \{\"name\": \"John\", \"age\": 30\} Input: 'Alice lives in Paris.' Output: \{\"name\": \"Alice\", \"city\": \"Paris\"\} Input: [Your Sentence Here] Output:"

3. Tree of Thoughts (ToT)​

Theory: Simulates exploring multiple branching paths of reasoning. Prompt: "Imagine three different experts are discussing this problem. Expert A proposes a conservative approach. Expert B proposes a radical approach. Expert C mediates. Write out their dialogue and reach a consensus."

4. Role-Based Prompting (The Actor)​

Theory: Assigning a specific persona constrains the output space to relevant professional standards. Prompt: "Act as a [Specific Role]. You have [Number] years of experience. You value [Value X] and [Value Y]."

5. The "Reflective" Pattern​

Theory: Asking the model to critique its own work before finalizing it. Prompt: "Write a draft of the email. Then, critique it for tone and clarity. Finally, rewrite the email incorporating your own feedback."

🛠️ Debugging Prompts​

  • Output too short? -> "Elaborate. Go deeper. Write a comprehensive guide."
  • Output too generic? -> "Be specific. Avoid generalities. Give concrete examples."
  • Hallucinating? -> "If you do not know the answer, say 'I don't know'. Do not make things up."