What's AI Podcast by Louis-François Bouchard

• AI Agents vs. Workflows: How to Spot Hype from Real "Agents"?

  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

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• CAG vs RAG: Which One is Right for You?

  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

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  9:49
• 7 Reasons Why Learning to Use LLMs Is a Game-Changer

  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.

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  9:19
• APIs 101: Deployment for AI Engineers

  When we talk about building powerful machine learning solutions, like large language models or retrieval-augmented generation, one key element that often flies under the radar is how to connect all the data and models and deploy them in a real product. This is where APIs come in. In this one, we’re diving into the world of APIs — what they are, why you might need one, and what deployment options are available. 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

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  14:25
• Do You REALLY Need an LLM?

  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/

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  9:15

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