Here's an overview of the impact of LLMs on human work, which is complex and varied across different job categories...

Software engineers vs. ML engineers vs. prompt engineers vs. LLM developers... all explained The rise of LLMs isn’t just about technology; it’s also about people. To unlock their full potential, we need a workforce with new skills and roles. This includes LLM Developers, who bridge the gap between software development, machine learning engineering, and prompt engineering. Let’s compare these roles... Master, Use and Build with LLMs in this Programming Language Agnostic Course: https://academy.towardsai.net/courses/8-hour-genai-primer?ref=1f9b29 Master LLMs and Get Industry-ready Now: https://academy.towardsai.net/?ref=1f9b29 Our ebook: https://academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29 Episode 2/6 of the "From Beginner to Advanced LLM Developer" course by Towards AI (linked above). This course is specifically designed as a 1 day bootcamp for Software Professionals (language agnostic). It is an efficient introduction to the Generative AI field. We teach the core LLM skills and techniques together with practical tips. This will prepare you to either use LLMs via natural language or to explore documentation for LLM model platforms and frameworks in the programming language of your choice and start developing your own customised LLM projects.

What most people call agents aren’t agents. I’ve never really liked the term “agent”, until I saw this recent article by Anthropic, where I totally agree and now see how we can call something an agent. The vast majority is simply an API call to a language model. It’s this. A few lines of code and a prompt. This cannot act independently, make decisions or do anything. It simply replies to your users. Still, we call them agents. But this isn’t what we need. We need real agents, but what is a real agent? That what we dive in into this episode... Links; Anthropic’s blog on agents: https://www.anthropic.com/research/building-effective-agents Anthropic’s computer use: https://www.anthropic.com/news/3-5-models-and-computer-use Hamul Husain’s log on Devin: https://www.answer.ai/posts/2025-01-08-devin.html

In the early days of LLMs, context windows, which is what we send them as text, were small, often capped at just 4,000 tokens (or 3,000 words), making it impossible to load all relevant context. This limitation gave rise to approaches like Retrieval-Augmented Generation (RAG) in 2023, which dynamically fetches the necessary context. As LLMs evolved to support much larger context windows—up to 100k or even millions of tokens—new approaches like caching, or CAG, began to emerge, offering a true alternative to RAG... ►Full article and references: https://www.louisbouchard.ai/cag-vs-rag/ ►Build Your First Scalable Product with LLMs: https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=1f9b29 ►Master LLMs and Get Industry-ready Now: https://academy.towardsai.net/?ref=1f9b29 ►Our ebook: https://academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29 ►Twitter: https://twitter.com/Whats_AI ►My Newsletter (My AI updates and news clearly explained): https://louisbouchard.substack.com/ ►Join Our AI Discord: https://discord.gg/learnaitogether

I think the first though about LLMs and generative AI, is often, “Cool tech buzzwords, but do I really need to know this?” YES. Here’s why diving into LLMs is practically essential... 🚀 1. They transform how we work Think about all the repetitive, boring tasks in your day. You can (almost) automate them, building tools that make you 10x more productive. That’s what LLMs can do. If you can't, someone else can. If it's too complex, it will be possible soon. 🧠 2. Reaching their full potential isn’t automatic LLMs don’t come with a magic "win button," even if ChatGPT by itself is fantastic. To use them effectively, you’ve got to understand what they’re good at, what they’re not, and how to make them work for you by adding features. ⚠️ 3. Misuse = trouble LLMs can mess up big time without the right skills—wrong answers, misinformation, or just plain inefficiency. Learning how to avoid these pitfalls is critical. ✍️ 4. Prompts are everything Crafting clear, precise instructions is half the battle. A well-thought-out prompt can turn mediocre results into game-changing insights. It's just the basics of good, clear and concise communication. 🎯 5. Knowing when to use them is key Not every problem needs AI, but knowing where LLMs can deliver the biggest impact? That’s a game-changer. The right tool at the right time = massive efficiency gains. 🔒 6. Privacy matters more than ever LLMs can accidentally expose sensitive information if you’re not careful. Learning to use them responsibly isn’t optional—it’s a must. (Unless you want to be the person who accidentally leaks proprietary data) ⏳ 7. Don’t get left behind Those who embrace and learn these tools early are already gaining a competitive edge. The ones who resist? Well... let’s say the AI train is moving fast, and you don’t want to be stuck at the station. I know LLMs can feel intimidating at first, but the rewards are worth it. Whether you’re a developer, a business leader, or just someone curious about the future, learning how to use these tools is a skill that’ll pay off in ways you can’t even imagine yet.

Explore the essential role of APIs in deploying powerful machine learning solutions. Discover how to connect data and models effectively. Learn deployment strategies, including options for serverless architecture. Get insights on major cloud platforms like AWS, Azure, and Google Cloud, tailored for developer needs. Understand when APIs are critical and when they may not be necessary. Perfect for anyone looking to build scalable products with large language models.

Build Your First Scalable Product with LLMs: https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=1f9b29 Master LLMs and Get Industry-ready Now: https://academy.towardsai.net/?ref=1f9b29 Our ebook: https://academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29 Video 8/10 of the "From Beginner to Advanced LLM Developer" course by Towards AI (linked above). The most practical and in-depth LLM Developer course out there (~90 lessons) for software developers, machine learning engineers, data scientists, aspiring founders or AI/Computer Science students. We’ve gathered everything we worked on building products and AI systems and put them into one super practical industry-focused course. Right now, this means working with Python, OpenAI, Llama 3, Gemini, Perplexity, LlamaIndex, Gradio, and many other amazing tools (we are unaffiliated and will introduce all the best LLM tool options). It also means learning many new non-technical skills and habits unique to the world of LLMs. Learn more for free... Twitter: https://x.com/Whats_AI Substack (newsletter): https://louisbouchard.substack.com/

In this one, I discuss the dilemma between using retrieval-based generation and the newer "long context models". Long context models, like the Gemini suite of models, allow us to send up to millions of tokens (thousands of text pages), whereas retrieval (RAG)-based systems allow us to search through as much (if not more) content and retrieve only the necessary bits to send the LLM for improved answers. Both have advantages and disadvantages. This short episode will help you better understand when to use each. Build Your First Scalable Product with LLMs: https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=1f9b29 Master LLMs and Get Industry-ready Now: https://academy.towardsai.net/?ref=1f9b29 Our ebook: https://academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29

► Get your copy of "Building LLMs for Production": https://amzn.to/4bqYU9b ►The e-book version: https://academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29 ► Our new course "From Beginners to Advanced LLM Developer": https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=1f9b29 ►Full article and references: https://www.louisbouchard.ai/openai-o1/ ►Twitter: https://twitter.com/Whats_AI ►My Newsletter (My AI updates and news clearly explained): https://louisbouchard.substack.com/ ►Join Our AI Discord: https://discord.gg/learnaitogether Extra Ressources: OpenAI release blog: https://openai.com/index/introducing-openai-o1-preview/  OpenAI release blog 2: https://openai.com/index/learning-to-reason-with-llms/  OpenAI system card: https://openai.com/index/openai-o1-system-card/  Nathan Lambert’s great article on it: https://www.interconnects.ai/p/openai-strawberry-and-inference-scaling-laws  David Shapiro fun livestream testing it: https://youtu.be/AO7mXa8BUWk How to start in AI/ML - A Complete Guide: ►https://www.louisbouchard.ai/learnai/ #gpt4o #o1 #openai

In this episode, Luis Serrano and I dive into the transformative impact of AI on education, forecasting a radical shift in how future generations learn and think. ► Luis' website: https://serrano.academy/ ►Twitter: https://twitter.com/Whats_AI ►My Newsletter (My AI updates and news clearly explained): https://louisbouchard.substack.com/ ►Support me on Patreon: https://www.patreon.com/whatsai ►Join Our AI Discord: https://discord.gg/learnaitogether How to start in AI/ML - A Complete Guide: ►https://www.louisbouchard.ai/learnai/ Chapters: 00:00 Coming up in the conversation 00:01:50 Sharing journey: Why Luis became an educator 00:06:03 Can someone develop skills to become a better educator, and what are they? 00:08:07 Deciding the depth of explanation 00:10:57 AI’s impact on education 00:22:35 How does an explanation without graphic aid look? 00:27:15 Luis is explaining embedding in an intuitive way? 00:31:05 Is AI hard to explain because of newness or complexity? 00:34:01 Necessity of understanding the basics of AI 00:36:57 Why do people not want to learn about how AI works? 00:39:15 Importance of good story telling and explanation 00:42:01 Strategy to explain tough topics 00:48:12 Strategy to introduce complex words in explanation 00:55:14 Evolution in AI Education Approaches  01:02:03 Is it possible to bring good value through shorts or reels? 01:04:46 Rise of Podcast and reels