Codingscape’s Post

Structure-Aware Multi-objective Metaprompt Optimization (#SAMMO) optimizes #prompts for #LLMs by leveraging their structure to guide optimization. This minimizes the time and effort needed to find performant prompts on a variety of tasks. https://msft.it/6040Y8y1y GitHub: https://lnkd.in/e9J9JtJr Paper: https://lnkd.in/efd5YcJW Central to SAMMO’s innovation is its approach to treating prompts not just as static text inputs but as dynamic, programmable entities—metaprompts. SAMMO represents these metaprompts as function graphs, where individual components and substructures can be modified to optimize performance, similar to the optimization process that occurs during traditional program compilation. #MicrosoftResearch #AI #GenerativeAI

🤔 Curious about how to extract precise outputs from AI models? 🔍Explore prompt engineering, fine-tuning, and RAG in our latest blog! 🔖Discover the pros and cons of each method and gain 💡insights on when and how to use them effectively. Dive in now👇 https://lnkd.in/gYRcmRNw #AI #PromptEngineering #FineTuning #RAG

An interesting read about how Anthropic’s Claude 3.5 outperforms other language models in generating complex, contextually rich text, and coding capabilities 🌟 Recently, it amazed users by its proficiency in writing code. 📝💻 Check out the full article here: https://lnkd.in/d8CsJ7B7 #AI #MachineLearning #NaturalLanguageProcessing #Innovation #TechTrends #Anthropic #Claude3.5

New paper out on prompting techniques for generative AI! 🤖 "The Prompt Report" provides the most comprehensive review to date of text-based prompting methods. Some key takeaways: - Prompting is the new programming. Over 200 prompting techniques analyzed. 🧑💻 - Clear taxonomy proposed with 6 major categories: In-Context Learning, Zero-Shot, Thought Generation, Decomposition, Ensembling, Self-Criticism. 🗂️ - Techniques also emerging for multilingual, multimodal, and conversational prompting. 🌐 10 notable text-based techniques covered: 1. 🔗 Chain-of-Thought (CoT) Prompting  2. 🔢 Few-Shot Prompting 3. 🎯 Zero-Shot CoT 4. 🤝 Self-Consistency  5. 🪜 Least-to-Most Prompting 6. 🌳 Tree-of-Thought 7. 🔄 Self-Refine 8. 🆚 Contrastive CoT 9. 🤖 Automatic CoT 10. 🧩 Ensemble Chain-of-Thought 🔖 Link to full paper: https://lnkd.in/gui5GX5P Definitely worth a read for anyone leveraging large language models for applications. The potential of prompting is immense and still rapidly evolving. 📈 This paper provides an invaluable reference. 📚

Great recap of #prompt techniques - see section 2.2. in particular if you are working on LLM applications.

Looking to build AI agent apps? Check out Kinfey Lo's latest post on understanding AI agents and how to create them with practical examples and code snippets. This post also explores the evolution of AI, the emergence of Language Model AI (LLM) and Copilot apps. Get started here: https://lnkd.in/g2Vwhn7E #AI #LLM #msftadvocate

Synth2: Pioneering Visual-Language Models with Synthetic Captions and Image Embeddings by Innovators at Google DeepMind #AI #AItechnology #artificialintelligence #Computationalresources #datascarcity #generativetext #GoogleDeepMind #imagemodels #llm #machinelearning #Synth2 #syntheticdata #VisualLanguageModels #VLMs

As companies look to integarte Artificial intelligence (AI) comapnies can find value & savings in many areas. AI is increasingly being integrated into software development processes, enhancing various stages from planning to maintenance. Here are some key ways AI is used in software development: 1. Code Generation and Autocompletion:   - Tools like GitHub Copilot & OpenAI's Codex help developers by suggesting code snippets, completing lines of code, and generating code based on natural language descriptions. 2. Bug Detection and Debugging:   - AI-powered tools can analyze code to identify potential bugs & vulnerabilities. Examples include DeepCode and Microsoft’s IntelliCode, which provide recommendations based on patterns learned from large codebases. 3. Testing and Quality Assurance:   - AI can automate the creation of test cases and scripts. Tools like Testim & Applitools use machine learning to detect changes in UI and predict areas of the code that might fail. 4. Predictive Analytics and Project Management:   - AI helps in predicting project timelines, identifying bottlenecks, & optimizing resource allocation. Tools like Jira & Asana incorporate AI to enhance project management and productivity. 5. Natural Language Processing (NLP):   - NLP models can improve documentation by generating & updating technical documents. They can also facilitate better communication through chatbots & virtual assistants that help developers with queries. 6. Refactoring and Code Optimization:   - AI can analyze code for performance improvements & suggest refactoring to enhance efficiency and readability. Tools like Embold help identify & resolve code issues. 7. Version Control and Code Merging:   - AI can assist in managing version control by predicting & resolving merge conflicts. It can also recommend optimal branching strategies & commit sequences. 8. Security Enhancement:   - AI can monitor & analyze code for security threats in real-time, providing insights & automated fixes for vulnerabilities. Tools like Snyk & Veracode integrate AI to improve software security. 9. Intelligent IDEs & Development Tools:   - Integrated Development Environments (IDEs) like Visual Studio & JetBrains incorporate AI to provide smart code suggestions, error detection, & context-aware assistance. 10. User Experience (UX) Design:    - AI can help design & optimize user interfaces by predicting user behavior & preferences. Tools like Adobe XD & Sketch use AI to enhance design elements & user interactions. AI in software development aims to increase productivity, reduce errors, & improve the overall quality of software products. As AI technologies continue to evolve, their impact on software development is expected to grow, making development processes more efficient and effective. A great example of how AI accomplishthis can be found in this podcast. https://lnkd.in/eJY9GsXQ

"Unlock the power of artificial intelligence with GPT-4! It's not just about automating tasks, it's about revolutionizing the way we visualize data. Instant map and chart creations are now a reality, all thanks to the progression of AI and machine learning. 🌐📊 Navigating the world of GPT apps and assistants can be challenging, but it doesn't have to be. At GPTs Playground, we've done the heavy lifting for you. We test, curate, and list the best GPT tools available, updating our list every day for your convenience. Join our community and experience the future of AI. You can even contribute by listing and upvoting your favorite GPTs! Check us out at www.gptsplayground.com and let's explore the AI playground together. 🚀🧠 #AI #MachineLearning #GPT4 #DataVisualization"

Looking to build AI agent apps? Check out Kinfey Lo's latest post on understanding AI agents and how to create them with practical examples and code snippets. This post also explores the evolution of AI, the emergence of Language Model AI (LLM) and Copilot apps. Get started here: https://lnkd.in/g2Vwhn7E #AI #LLM #msftadvocate