Master the art and science of prompting LLMs. Covers zero-shot, few-shot, chain-of-thought, RAG, ReAct, model-specific strategies, safety, and production deployment.
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Understand how LLMs interpret prompts, master zero-shot and few-shot techniques, and learn the core design principles that make every prompt more effective. Reference: https://github.com/dair-ai/Prompt-Engineering-Guide
Unlock step-by-step reasoning in LLMs with chain-of-thought, self-consistency, tree of thoughts, ReAct, and prompt chaining. Reference: https://github.com/dair-ai/Prompt-Engineering-Guide
Go beyond basics with RAG, program-aided language models, active prompting, directional stimulus, automatic prompt optimization, and multimodal prompting. Reference: https://github.com/dair-ai/Prompt-Engineering-Guide
Apply prompt engineering to code generation, data augmentation, classification, summarization, and creative writing tasks. Reference: https://github.com/dair-ai/Prompt-Engineering-Guide
Learn the unique prompting techniques, strengths, and quirks of GPT-4, Claude, Gemini, and open-source models like LLaMA and Mistral. Reference: https://github.com/dair-ai/Prompt-Engineering-Guide
Defend against prompt injection, mitigate hallucination and bias, implement content safety guardrails, and practice responsible AI deployment. Reference: https://github.com/dair-ai/Prompt-Engineering-Guide
Take prompts to production with template management, evaluation frameworks, cost optimization, application architecture, and real-world case studies. Reference: https://github.com/dair-ai/Prompt-Engineering-Guide