Andrew Ng’s Post

#Building #AI #Applications with #Haystack: A Comprehensive Course #Introduction** The rapidly evolving landscape of generative AI technology often presents challenges in integrating APIs from various #LLMs, #vector #databases, and other tools. To address these complexities and streamline the development process, #Haystack, a powerful #open-#source #framework, offers a modular approach. This new short course, "#Building #AI #Applications with #Haystack," provides a deep dive into the framework, equipping learners with the skills to create sophisticated AI applications. #Key Course Objectives** #Mastering Haystack Building Blocks:** Understand the core components, pipelines, and document stores that form the foundation of Haystack applications. #Creating Custom Components:** Learn to extend the capabilities of your application by building custom components, such as a Hacker News summarizer. #Implementing Conditional Routing:** Explore how to create branching pipelines that adapt to different scenarios, including fallback mechanisms for when LLMs lack sufficient context. #Building Self-Reflecting Agents:** Develop agents that can self-evaluate their performance and refine their responses through custom components like output validators. #Leveraging OpenAI Function Calling:** Utilize OpenAI's function-calling capabilities to integrate Haystack pipelines as tools for LLMs, enhancing their overall functionality. #Course Benefits** By the end of this course, learners will gain a comprehensive understanding of the Haystack framework and its applications. They will be able to: #Build Flexible Applications:** Create AI applications that can easily adapt to changing technology stacks and user needs. #Manage Complexity:** Effectively handle the complexities of integrating various AI components and tools. #Enhance Application Capabilities:** Extend the functionality of their applications by developing custom components and leveraging advanced features. #Ensure Maintainability:** Build applications that are easier to maintain and update as new features and technologies emerge. #Conclusion** "#Building AI Applications with #Haystack" offers a valuable opportunity to learn a flexible and efficient framework for developing AI applications. By mastering the concepts and techniques presented in this course, learners can create innovative and powerful solutions that meet the evolving demands of the AI landscape.

If you want to get started building your own AI applications, start with an easy-to-use framework from Haystack by deepset But before that, take this short course by Tuana Çelik and Andrew Ng

Love seeing Andrew Ng and the DeepLearning.AI partnership with deepset to discuss #haystack our open source framework for AI. Kudos to Tuana Çelik and our OS team for pioneering this session and their thought leadership!

Build and customize complex AI applications with a flexible framework in this new short course, "Building AI Applications with Haystack."

Thanks for sharing this goldmine of insights on AI Agents and workflows! 🚀 Each tip here is a valuable piece for anyone building scalable and efficient AI agent systems. Let’s break down some of the key technical highlights: 1️⃣ Prompt Design: Clarity is King 📝 Simplifying prompts is fundamental to achieving reliable agent behavior. Overly complex instructions often lead to ambiguous or inconsistent outputs. Clear, goal-oriented prompts ensure that agents understand and execute tasks with precision, making workflows predictable and repeatable. 2️⃣ Data-Driven Reliability 📊 Incorporating real-world data and leveraging structured datasets dramatically improves agent reliability. Techniques like Retrieval-Augmented Generation (RAG) ensure outputs remain up-to-date, contextually aware, and factually accurate, bridging the gap between static knowledge and live data. 3️⃣ Workflow Optimization and Flow Engineering ⚙️ Building and testing workflows incrementally, rather than in one monolithic integration, ensures that each agent and tool works as intended. Flow engineering provides clarity, reduces errors, and allows teams to visualize and debug processes effectively before deployment. 4️⃣ Performance Tuning and Specialized LLMs ⚡ Choosing task-specific LLMs (e.g., models optimized for reasoning, creativity, or multimodal tasks) is crucial for maximizing performance. Tools like Groq and Ollama further accelerate response times, making workflows not just efficient but lightning-fast. 5️⃣ Vector Database Configuration 🗂️ Effective vector database management is critical for tasks like document retrieval and knowledge graph construction. Tuning parameters such as embedding quality, chunk size, and chunk overlap ensures that retrieval remains precise and contextually relevant. 6️⃣ Speech and Advanced Retrieval Integration 🎙️ The integration of speech and audio interfaces adds a layer of dynamism, enhancing agent-human interactions. Combining dense passage retrieval (DPR) with RAG workflows delivers precision and accuracy in large-scale information retrieval scenarios. This tutorial is a must-watch for anyone serious about building robust AI agent systems. The practical tips shared here lay a strong foundation for creating scalable, efficient, and adaptable workflows. Thanks again for sharing this resource-packed guide! 🧠📚 #AIAgents #LangChain #LangGraph #CrewAI #RAG #LLM #WorkflowEngineering #AIOptimization #TechInnovation 🚀

🚀 About building robust GenAI applications updates! 🤖 Building robust, observable, and scalable GenAI applications is challenging. This blog post guides you in creating enterprise-grade GenAI solutions using PromptFlow and LangChain, emphasizing observability, trackability, model monitoring, debugging, and autoscaling. It highlights that PromptFlow and AI Studio can enhance enterprise GenAI applications, regardless of using LangChain, OpenAI SDK, or Llama Index. https://lnkd.in/dXNNYbCK 🌟 #AI #GenAI #EnterpriseSolutions #llms

AI Agents: Why Now—and How to Get Started 2025 is proving that smart trumps big in the AI world. Here’s why: Open Source Momentum Models like DeepSeek-R1 make high-end LLM capabilities affordable and customizable. ✅Model Distillation Smaller, cheaper models are matching “giant LLM” performance—shifting focus to efficiency and accessibility. ✅Computer Use & Action AI is now navigating browsers and apps (see Anthropic’s desktop agent, OpenAI’s Operator), going well beyond just text output. ✅Growing Global Competition As open weights become the norm, the real innovation is moving to agent applications—where domain-specific use cases shine. ✅Shift from Brute Force to Clever Solutions Fine-tuning and distillation techniques let you build specialized systems without a massive budget. GamePlan: Where to Go Next Hands-On Practice Start small. For example, build a single-agent task manager that automates repetitive workflows. Leverage Frameworks Use no/low-code platforms (Beam AI, Relevance AI) to quickly prototype—but take time to understand the fundamentals under the hood. Explore Our New Course LLM Performance Evaluation in our Custom AI Agent Academy community. 👉https://lnkd.in/epfPYbMH Learn how to analyze, fine-tune, and optimize agent performance efficiently. Stay Informed Subscribe to newsletters like AI Agent Insider for the latest breakthroughs, open-source releases, and best practices. Bottom Line We’re at a turning point where AI agents can truly act on your behalf—scheduling tasks, browsing the web, using software, and more. Thanks to open models, distillation, and clever architecture, there’s never been a better time to jump in. 🫵Over to You Which trend do you find most challenging right now? Have you tried new open-source agents or specialized frameworks? What skills beyond prompt engineering do you think are crucial? Drop your thoughts below—let’s shape the next generation of AI agents, together!

I've really enjoyed getting familiar with LangGraph for AI application development! It really is a sweet spot: providing just enough of a framework to allow for quick development, while still leaving plenty of flexibility to suit multiple use cases. My notes below on how to start building: https://lnkd.in/eq2x_K7K

🤖 Comparing AI Agent Frameworks: Bee Agent, CrewAI, Autogen, and Swarm As we explore the future of AI, building intelligent agents is becoming increasingly important. Here’s a quick comparison of four powerful frameworks to help you build the AI of tomorrow. Dive into the details and discover which one might be best for your next project! 🌐👇 🔸 Bee Agent 🔗 https://lnkd.in/d5N6b3PD • Works with various AI models • Offers tools for coding, memory management, and workflow control • Supports tool-building across multiple programming languages • Highly customizable but may have a steeper learning curve 🔸 CrewAI 🔗 https://lnkd.in/dZmysWPy • Built on LangChain • Enables creation of agents with defined roles and tasks • Ideal for managing complex workflows • Supports a wide range of AI models 🔸 Autogen 🔗 https://lnkd.in/dyrdThcE • Focused on open-source models • Suitable for creating large, distributed agent networks • Operates across different programming languages • Comes with tools for tracking and troubleshooting 🔸 Swarm 🔗 https://lnkd.in/djeWysGa • Designed to integrate with OpenAI’s GPT-4 • Capable of handling numerous independent tasks • Provides fine-grained control over agent behavior • Experimental and not production-ready Why it matters: These frameworks represent a shift towards more flexible, scalable, and robust multi-agent systems. They each bring unique strengths, catering to various needs from complex workflow management to large-scale, distributed AI networks. Which framework do you find the most exciting? Let’s discuss! 🌍💬 #AI #MachineLearning #AIFrameworks #ArtificialIntelligence #OpenSourceAI #IntelligentAgents #LangChain #OpenAI #Autogen #SwarmAI

Your terminal just got smarter! 🧠💻 Introducing gptme: an open-source tool that let's you run AI Agents in your terminal with local tools Here's why it's a game-changer: 🔓 Free and open-source 💻 Runs code in your local environment 📝 Reads, writes, and edits files 🌐 Browses the web from your terminal 👀 Vision capabilities—interprets images and screenshots 🔄 Self-correcting AI assistant 🤖 Supports multiple LLMs (OpenAI, Anthropic, OpenRouter, local models) But that's not all: 🎯 Shell Copilot—find shell commands with natural language 🖥️ Development aid—write, test, and run code with AI 📊 Easy data analysis on local files 🚀 Extensible with new tools 💬 Optional Web UI and REST API 🛠️ Developer-friendly—clean codebase, easy contributions Link to the GitHub repo: https://lnkd.in/gMS379MC Find me → https://lnkd.in/em_V4unu  ✔️ For more insights & tutorials on AI and Machine Learning.