A Complete Guide to Meta Prompting
a year ago
- #Prompt Engineering
- #LLM
- #AI Optimization
- Meta prompting uses LLMs to create and refine prompts dynamically.
- PromptHub's Prompt Iterator automates prompt refinement based on user feedback.
- Meta-Prompting involves a central LLM coordinating expert LLMs for complex tasks.
- Learning from Contrastive Prompts (LCP) refines prompts by comparing good and bad examples.
- Automatic Prompt Engineer (APE) optimizes prompts through iterative generation and scoring.
- PromptAgent uses expert-level feedback to refine prompts in a tree-like structure.
- Conversational Prompt Engineering (CPE) refines prompts via interactive chat.
- DSPy manages complex LLM pipelines programmatically for adaptive prompt workflows.
- TEXTGRAD uses natural language feedback as 'textual gradients' for prompt refinement.
- Prompt generators like PromptHub, Anthropic, and OpenAI streamline meta prompting.