Meta Prompting: Revolutionizing LLM Prompt Engineering
This article explores meta prompting, a technique using Large Language Models (LLMs) to create and refine prompts. It details various meta-prompting methods, including the Stanford and OpenAI collaboration's method using a 'conductor' LLM to orchestrate expert LLMs; Amazon's Learning from Contrastive Prompts (LCP), which improves prompts by comparing good and bad ones; Automatic Prompt Engineer (APE), Prompt Agent, Conversational Prompt Engineering (CPE), DSPy, and TEXTGRAD. The article compares their strengths and weaknesses, highlighting how these methods significantly improve prompt engineering efficiency. Finally, it showcases prompt generation tools from platforms like PromptHub, Anthropic, and OpenAI, simplifying meta-prompting implementation and unlocking the full potential of LLMs.
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