The practice of designing and refining inputs (prompts) to effectively communicate with and guide AI systems, particularly large language models, to generate desired outputs. This involves crafting specific instructions, examples, and context to elicit accurate, helpful, and appropriate responses.
• Improves AI output quality and relevance without modifying the underlying model
• Reduces hallucinations and factual errors in AI-generated content
• Enables more precise control over AI behavior and response patterns
• Facilitates complex task completion through well-structured instructions
• Enhances consistency and reliability of AI outputs across various use cases
• Large Language Model (LLM)
• Generative AI
• Fine-tuning
• Foundation Model
• Natural Language Processing (NLP)
• Prompt Engineering Best Practices
• Advanced Techniques for LLM Prompting
• Prompt Engineering vs. Fine-tuning: A Comparison
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