{
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  "title": "Prompt Engineering Guide | Prompt Engineering Guide<!-- -->",
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  "content": "🚀 Learn to build apps with Claude Code! Use PROMPTING for 20% off Enroll now → !function(){try{var d=document.documentElement,c=d.classList;c.remove('light','dark');var e=localStorage.getItem('theme');if('system'===e||(!e&&true)){var t='(prefers-color-scheme: dark)',m=window.matchMedia(t);if(m.media!==t||m.matches){d.style.colorScheme = 'dark';c.add('dark')}else{d.style.colorScheme = 'light';c.add('light')}}else if(e){c.add(e|| '')}if(e==='light'||e==='dark')d.style.colorScheme=e}catch(e){}}() document.documentElement.setAttribute('dir','ltr') Prompt Engineering Guide 🎓 Courses About About GitHub GitHub (opens in a new tab) Discord Discord (opens in a new tab) ✨ Services Prompt Engineering Introduction LLM Settings Basics of Prompting Prompt Elements General Tips for Designing Prompts Examples of Prompts Prompting Techniques Zero-shot Prompting Few-shot Prompting Chain-of-Thought Prompting Meta Prompting Self-Consistency Generate Knowledge Prompting Prompt Chaining Tree of Thoughts Retrieval Augmented Generation Automatic Reasoning and Tool-use Automatic Prompt Engineer Active-Prompt Directional Stimulus Prompting Program-Aided Language Models ReAct Reflexion Multimodal CoT Graph Prompting AI Agents Introduction to Agents Agent Components AI Workflows vs AI Agents Context Engineering for AI Agents Context Engineering Deep Dive Function Calling Deep Agents Guides Optimizing Prompts OpenAI Deep Research Reasoning LLMs 4o Image Generation Context Engineering Guide Applications Fine-tuning GPT-4o Function Calling Context Caching with LLMs Generating Data Generating Synthetic Dataset for RAG Tackling Generated Datasets Diversity Generating Code Graduate Job Classification Case Study Prompt Function Prompt Hub Classification Sentiment Classification Few-Shot Sentiment Classification Coding Generate Code Snippet Generate MySQL Query Draw TiKZ Diagram Creativity Rhymes Infinite Primes Interdisciplinary Inventing New Words Evaluation Evaluate Plato's Dialogue Information Extraction Extract Model Names Image Generation Draw a Person Using Alphabet Mathematics Evaluating Composite Functions Adding Odd Numbers Question Answering Closed Domain Question Answering Open Domain Question Answering Science Question Answering Reasoning Indirect Reasoning Physical Reasoning Text Summarization Explain A Concept Truthfulness Hallucination Identification Adversarial Prompting Prompt Injection Prompt Leaking Jailbreaking Models ChatGPT Claude 3 Code Llama Flan Gemini Gemini Advanced Gemini 1.5 Pro Gemma GPT-4 Grok-1 Kimi K2.5 LLaMA Llama 3 Mistral 7B Mistral Large Mixtral Mixtral 8x22B OLMo Phi-2 Sora LLM Collection Risks & Misuses Adversarial Prompting Factuality Biases LLM Research Findings LLM Agents RAG for LLMs LLM Reasoning RAG Faithfulness LLM In-Context Recall RAG Reduces Hallucination Synthetic Data ThoughtSculpt Infini-Attention LM-Guided CoT Trustworthiness in LLMs LLM Tokenization What is Groq? Papers Tools Notebooks Datasets Additional Readings Services English Light Question? Give us feedback → (opens in a new tab) Edit this page Prompt Engineering Copy page Prompt Engineering Guide Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs). Researchers use prompt engineering to improve the capacity of LLMs on a wide range of common and complex tasks such as question answering and arithmetic reasoning. Developers use prompt engineering to design robust and effective prompting techniques that interface with LLMs and other tools. Prompt engineering is not just about designing and developing prompts. It encompasses a wide range of skills and techniques that are useful for interacting and developing with LLMs. It's an important skill to interface, build with, and understand capabilities of LLMs. You can use prompt engineering to improve safety of LLMs and build new capabilities like augmenting LLMs with domain knowledge and external tools. Motivated by the high interest in developing with LLMs, we have created this new prompt engineering guide that contains all the latest papers, advanced prompting techniques, learning guides, model-specific prompting guides, lectures, references, new LLM capabilities, and tools related to prompt engineering. Related Learning Course Prompt Engineering for LLMs Learn essential prompt engineering techniques to get the most out of large language models. From basic prompting to advanced strategies. Beginner 2 hours Course Building Effective AI Agents Learn to build effective AI agents. Covers function calling, tool integration, and debugging agentic systems. Intermediate 5 hours Explore All Courses Discover our full catalog of AI and prompt engineering courses. From beginners to advanced practitioners. Use code PROMPTING20 for 20% off! Browse Academy Int...",
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