The Prompt Report: A Systematic Survey of Prompting Techniques "The Prompt Report," is the result of an extensive study analyzing over 1,500 academic papers on prompting conducted by a team of researchers from leading institutions. It introduces a structured taxonomy of 58 prompting techniques organized into six categories, highlighting effective strategies such as Few-Shot and Chain-of-Thought prompting. Additionally, the report discusses the importance of In-Context Learning and assesses the performance of various prompting techniques while offering insights into future challenges and developments in human-AI interactions. Key Points - A systematic analysis resulted in the creation of "The Prompt Report," detailing over 58 prompting techniques within a comprehensive 80-page document. - The report categorizes prompting techniques into six problem-solving areas, enabling users to identify and apply the best methods for their tasks. - In-Context Learning (ICL) allows language models to perform tasks based on examples within prompts, emphasizing the power of Few-Shot techniques. - Effective prompt design hinges on various factors including the quality, quantity, and format of examples used in prompts. - Benchmarking results reveal Few-Shot Chain-of-Thought techniques as superior for reasoning tasks, while Self-Consistency is less effective than anticipated. - Comparisons show that AI-driven prompt engineering tools can outperform manual efforts, highlighting advancements in automated prompting techniques. - The report addresses future challenges in prompting, including prompt drift, multilingual applications, and integrating multimodal inputs. https://learnprompting.org/blog/the_prompt_report https://huggingface.co/papers/2406.06608 https://www.researchgate.net/publication/381318099_The_Prompt_Report_A_Systematic_Survey_of_Prompting_Techniques #PromptReport #PromptEngineering #InContextLearning #FewShotLearning #ChainOfThought #PromptingTechniques #AI #NLP #LanguageModels #HumanAIInteraction #PromptDrift #MultilingualAI #LearnPrompting #AISurvey #PromptTaxonomy #SelfConsistency #PromptTools

Microsoft offers a variety of resources and courses for beginners interested in AI agents. Their learning platform, Microsoft Learn, provides structured modules that cover the basics of AI and how to build intelligent agents using tools like Azure Cognitive Services and Bot Framework . These courses are designed to help newcomers understand key concepts in AI, including natural language processing and machine learning . Additionally, Microsoft has created hands-on labs and tutorials that guide users through the process of developing their own AI applications . This practical approach not only enhances learning but also equips beginners with the skills needed to implement AI solutions in real-world scenarios . Overall, Microsoft's commitment to education in AI ensures that aspiring developers have access to comprehensive resources to kickstart their journey into the world of AI agents. https://microsoft.github.io/ai-agents-for-beginners/ https://microsoft.github.io/AI-For-Beginners/ https://github.com/microsoft/ai-agents-for-beginners #microsoftlearn #aieducation #aiagents #azurecognitiveservices #botframework #nlp #machinelearning #aimicrosoft #airealworld #ai #aiagentsforbeginners #hands-onlabs #aitutorials #googlecloudai #awsai #cortana

Claims are awash on X about how the latest Kimi Model K2 beats Grok 4. My verdict ... try it for yourself ! l also noticed that some models are better in certain contexts than others but fail in other context where others excel. So my take is to know which is which for yourself .. don' t just rely on Benchmarks, social media posters and such ... Now back to Kimi AI the chinese controversial release: Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained with the Muon optimizer, Kimi K2 achieves exceptional performance across frontier knowledge, reasoning, and coding tasks while being meticulously optimized for agentic capabilities. Key Features Large-Scale Training: Pre-trained a 1T parameter MoE model on 15.5T tokens with zero training instability. MuonClip Optimizer: We apply the Muon optimizer to an unprecedented scale, and develop novel optimization techniques to resolve instabilities while scaling up. Agentic Intelligence: Specifically designed for tool use, reasoning, and autonomous problem-solving. Model Variants Kimi-K2-Base: The foundation model, a strong start for researchers and builders who want full control for fine-tuning and custom solutions. Kimi-K2-Instruct: The post-trained model best for drop-in, general-purpose chat and agentic experiences. It is a reflex-grade model without long thinking. https://github.com/MoonshotAI/Kimi-K2 https://www.moonshot.ai/ https://platform.moonshot.ai/docs/introduction#text-generation-model https://github.com/MoonshotAI/Kimi-K2/blob/main/docs/deploy_guidance.md(Deployment Guide) #KimiK2 #KimiAI #MoonshotAI #Grok4 #LLM #LargeLanguageModel #MoE #MixtureOfExperts #AI #AgenticAI #MuonOptimizer #AICoding #Chatbot #KimiK2Base #KimiK2Instruct #TextGeneration #FrontierKnowledge #Reasoning #AutonomousProblemSolving #TransformerModel #Claude #Gemini #GPT4 #OpenAI #Llama3 #AIModels #GitHub #X #SocialMedia #Benchmarks #ControversialRelease #ChineseAI #AIInnovation #DeepLearning #NaturalLanguageProcessing #NLP #AIResearch #AIML #MachineLearning #DataScience #ArtificialIntelligence #BigData #Technology #Innovation #Tech #AISolutions #DigitalTransformation #AICommunity #AINews #EmergingTech #TechTrends

Cheers to another viral Prompt Post : PROMPT: You are Lyra, a master-level AI prompt optimization specialist. Your mission: transform any user input into precision-crafted prompts that unlock AI's full potential across all platforms. ## THE 4-D METHODOLOGY ### 1. DECONSTRUCT - Extract core intent, key entities, and context - Identify output requirements and constraints - Map what's provided vs. what's missing ### 2. DIAGNOSE - Audit for clarity gaps and ambiguity - Check specificity and completeness - Assess structure and complexity needs ### 3. DEVELOP - Select optimal techniques based on request type: - **Creative** → Multi-perspective + tone emphasis - **Technical** → Constraint-based + precision focus - **Educational** → Few-shot examples + clear structure - **Complex** → Chain-of-thought + systematic frameworks - Assign appropriate AI role/expertise - Enhance context and implement logical structure ### 4. DELIVER - Construct optimized prompt - Format based on complexity - Provide implementation guidance ## OPTIMIZATION TECHNIQUES **Foundation:** Role assignment, context layering, output specs, task decomposition **Advanced:** Chain-of-thought, few-shot learning, multi-perspective analysis, constraint optimization **Platform Notes:** - **ChatGPT/GPT-4:** Structured sections, conversation starters - **Claude:** Longer context, reasoning frameworks - **Gemini:** Creative tasks, comparative analysis - **Others:** Apply universal best practices ## OPERATING MODES **DETAIL MODE:** - Gather context with smart defaults - Ask 2-3 targeted clarifying questions - Provide comprehensive optimization **BASIC MODE:** - Quick fix primary issues - Apply core techniques only - Deliver ready-to-use prompt ## RESPONSE FORMATS **Simple Requests:** ``` **Your Optimized Prompt:** [Improved prompt] **What Changed:** [Key improvements] ``` **Complex Requests:** ``` **Your Optimized Prompt:** [Improved prompt] **Key Improvements:** • [Primary changes and benefits] **Techniques Applied:** [Brief mention] **Pro Tip:** [Usage guidance] ``` ## WELCOME MESSAGE (REQUIRED) When activated, display EXACTLY: "Hello! I'm Lyra, your AI prompt optimizer. I transform vague requests into precise, effective prompts that deliver better results. **What I need to know:** - **Target AI:** ChatGPT, Claude, Gemini, or Other - **Prompt Style:** DETAIL (I'll ask clarifying questions first) or BASIC (quick optimization) **Examples:** - "DETAIL using ChatGPT — Write me a marketing email" - "BASIC using Claude — Help with my resume" Just share your rough prompt and I'll handle the optimization!" ## PROCESSING FLOW 1. Auto-detect complexity: - Simple tasks → BASIC mode - Complex/professional → DETAIL mode 2. Inform user with override option 3. Execute chosen mode protocol 4. Deliver optimized prompt **Memory Note:** Do not save any information from optimization sessions to memory. https://x.com/minchoi/status/1940251593431257164 #Lyra #AIPromptOptimization #PromptEngineering #ChatGPT #Claude #Gemini #AIMastery #PromptDesign #AItools #PromptTechniques #NLP #AIassistance

Check this Repo out ! Shubham Saboo's GitHub repository hosts a collection of LLM Apps. These applications leverage large language models (LLMs) from various providers, including OpenAI, Anthropic, and Google, and open-source models. The apps incorporate Retrieval-Augmented Generation (RAG), AI agents, multi-agent teams, and voice agent technologies. This curated list is a valuable resource for exploring the capabilities and applications of LLMs in different contexts. #AwesomeLLMApps #LLM #LargeLanguageModels #OpenAI #Anthropic #GoogleAI #RAG #RetrievalAugmentedGeneration #AIAgents #MultiAgentTeams #VoiceAgents #GitHub #MachineLearning #NLP #AI #Langchain #LlamaIndex #DeepLearning #AISolutions https://github.com/Shubhamsaboo/awesome-llm-apps

Unlock the potential of AI with Anthropic's guide to prompt engineering for Claude! 🌟 This comprehensive resource helps developers craft effective prompts to maximize Claude's capabilities in various applications. Learn key strategies for optimizing your prompts to improve response quality and relevance, whether you're focused on creative writing, coding assistance, or more specialized tasks. With practical examples and detailed tips, you'll gain the insights needed to create powerful interactions with AI. Start enhancing your projects with Claude's advanced capabilities today! 🤖✨ #AIPromptEngineering #ClaudeAI #MachineLearning #TechInnovation #Anthropic#anthropic #claude #promptengineering #ai #artificialintelligence #promptdesign #machinelearning #largeLanguageModels #llm #nlp #naturalLanguageProcessing #promptoptimization #airesearch #aitools #googlebard #chatgpt #gemini #aiwriting #aicoding #aichatbot https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview

🚀 Explore the world of AI with local, open-source alternatives to ChatGPT! 🖥️ These tools offer privacy, offline access, and full control over your chatbot experience. Here are 10 of the best options from a recent roundup: Gaia by AMD - A user-friendly model running fully locally on Windows PCs, optimized for Ryzen AI. Ollama - A sleek interface for LLaMA, Mistral, and Gemma, making local LLM usage simple and efficient. LM Studio - A GUI application that supports various models and offers an intuitive user experience. LocalAI - An API-compatible tool that allows easy integration of local LLMs into your applications. Text Generation Web UI (oobabooga) - A feature-rich local LLM with extensive plugin support and community-driven enhancements. PrivateGPT - Offers a fully offline AI experience with document querying capabilities, perfect for sensitive data. GPT4All - A plug-and-play solution for using multiple LLMs seamlessly on consumer hardware. Jan - A beautiful local assistant with a focus on code assistance, catering especially to macOS users. Hermes/KoboldAI Horde - Designed for storytelling and dialogue, perfect for writers and creatives. Chatbot UI + Ollama Backend - A ChatGPT-style interface for a personalized local chatbot experience. These tools not only protect your privacy but also empower you to customize your AI interactions. Dive into the open-source revolution! #AI #ChatGPT #OpenSource #LocalAI #Privacy #MachineLearning #LLM #NLP #DevTools #localai #opensource #chatgpt #llm #ai #gaia #ollama #lmstudio #textgenerationwebui #privategpt #gpt4all #jan #hermes #koboldai #chatbotui #amd #ryzenai #llama #mistral #gemma #macos #offlineai #aicoding #chatbot #localmodels https://dev.to/therealmrmumba/10-best-open-source-chatgpt-alternative-that-runs-100-locally-jdc