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Beginner1 Hour 34 Minutes7 Video LessonsInstructors: Michele Catasta, Matt PalmerReplit Build and share two applications—a website performance analyzer and a voting app—while using an AI coding agent to debug, customize, and strengthen your coding skills. Learn the principles of agentic code development and skills to effectively build, host, and share your apps with Replit coding agents and assist

Dear friends, The buzz over DeepSeek this week crystallized, for many people, a few important trends that have been happening in plain sight: (i) China is catching up to the U.S. in generative AI, with implications for the AI supply chain. (ii) Open weight models are commoditizing the foundation-model layer, which creates opportunities for application builders. (iii) Scaling up isn’t the only path

In AI Agentic Design Patterns with AutoGen you’ll learn how to build and customize multi-agent systems, enabling agents to take on different roles and collaborate to accomplish complex tasks using AutoGen, a framework that enables development of LLM applications using multi-agents. In this course you’ll create: A two-agent chat that shows a conversation between two standup comedians, using “Conver

Dear friends, In the last couple of days, Google announced a doubling of Gemini Pro 1.5's input context window from 1 million to 2 million tokens, and OpenAI released GPT-4o, which generates tokens 2x faster and 50% cheaper than GPT-4 Turbo and natively accepts and generates multimodal tokens. I view these developments as the latest in an 18-month trend. Given the improvements we've seen, best pra

Agentic Design Patterns Part 1 Four AI agent strategies that improve GPT-4 and GPT-3.5 performance Dear friends, I think AI agent workflows will drive massive AI progress this year — perhaps even more than the next generation of foundation models. This is an important trend, and I urge everyone who works in AI to pay attention to it. Today, we mostly use LLMs in zero-shot mode, prompting a model t

Retrieval Augmented Generation (RAG) stands out as one of the most popular use cases of large language models (LLMs). This method facilitates the integration of an LLM with an organization’s proprietary data. To successfully implement RAG, it is essential to enhance retrieval techniques for obtaining coherent contexts and employ effective evaluation metrics. In this course, we’ll explore: Two adva

Generative AI for EveryoneLearn how generative AI works, and how to use it in your life and at work Enroll Now Learn directly from Andrew Ng about the technology of generative AI, how it works, and what it can (and can’t) do Instructed by AI pioneer Andrew Ng, Generative AI for Everyone offers his unique perspective on empowering you and your work with generative AI. Andrew will guide you through

Join our new short course, Finetuning Large Language Models! Learn from Sharon Zhou, Co-Founder and CEO of Lamini, and instructor for the GANs Specialization and How Diffusion Models Work. When you complete this course, you will be able to: Understand when to apply finetuning on LLMs Prepare your data for finetuning Train and evaluate an LLM on your data With finetuning, you’re able to take your o

In LangChain for LLM Application Development, you will gain essential skills in expanding the use cases and capabilities of language models in application development using the LangChain framework. In this course you will learn and get experience with the following topics: Models, Prompts and Parsers: calling LLMs, providing prompts and parsing the response Memories for LLMs: memories to store con

In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications.  Using the OpenAI API, you’ll be able to quickly build capabilities that learn to innovate and create value in ways that were cost-prohibitive, highly technical, or simply impossible before now. This short course taught by Isa Fulford (OpenAI) and And

Dear friends, Recent successes with large language models have brought to the surface a long-running debate within the AI community: What kinds of information do learning algorithms need in order to gain intelligence? The vast majority of human experience is not based on language. The taste of food, the beauty of a sunrise, the touch of a loved one — such experiences are independent of language. B

Inside the Mind of Claude, Llama 4’s Mixture of Vision-Language Experts, More Open Multimodal Models, Neural Net for Tabular Data

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