Crossover with Embedded AI Podcast

In this special crossover episode with the brand-new Embedded AI Podcast, Luca and Jeff are joined by Ryan Torvik, Luca's co-host on the Embedded AI podcast, to explore the intersection of AI-powered development tools and agile embedded systems engineering. The hosts discuss practical strategies for using Large Language Models (LLMs) effectively in embedded development workflows, covering topics like context management, test-driven development with AI, and maintaining code quality standards in safety-critical systems. The conversation addresses common anti-patterns that developers encounter when first adopting LLM-assisted coding, such as "vibe coding" yourself off a cliff by letting the AI generate too much code at once, losing control of architectural decisions, and failing to maintain proper test coverage. The hosts emphasize that while LLMs can dramatically accelerate prototyping and reduce boilerplate coding, they require even more rigorous engineering discipline - not less. They discuss how traditional agile practices like small commits, continuous integration, test-driven development, and frequent context resets become even more critical when working with AI tools. For embedded systems engineers working in safety-critical domains like medical devices, automotive, and aerospace, the episode provides valuable guidance on integrating AI tools while maintaining deterministic quality processes. The hosts stress that LLMs should augment, not replace, static analysis tools and human code reviews, and that developers remain fully responsible for AI-generated code. Whether you're just starting with AI-assisted development or looking to refine your approach, this episode offers actionable insights for leveraging LLMs effectively while keeping the reins firmly in hand. ## Key Topics * [03:45] LLM Interface Options: Web, CLI, and IDE Plugins - Choosing the Right Tool for Your Workflow* [08:30] Prompt Engineering Fundamentals: Being Specific and Iterative with LLMs* [12:15] Building Effective Base Prompts: Learning from Experience vs. Starting from Templates* [16:40] Context Window Management: Avoiding Information Overload and Hallucinations* [22:10] Understanding LLM Context: Files, Prompts, and Conversation History* [26:50] The Nature of Hallucinations: Why LLMs Always Generate, Never Judge* [29:20] Test-Driven Development with AI: More Critical Than Ever* [35:45] Avoiding 'Vibe Coding' Disasters: The Importance of Small, Testable Increments* [42:30] Requirements Engineering in the AI Era: Becoming More Specific About What You Want* [48:15] Extreme Programming Principles Applied to LLM Development: Small Steps and Frequent Commits* [52:40] Context Reset Strategies: When and How to Start Fresh Sessions* [56:20] The V-Model Approach: Breaking Down Problems into Manageable LLM-Sized Chunks* [01:01:10] AI in Safety-Critical Systems: Augmenting, Not Replacing, Deterministic Tools* [01:06:45] Code Review in the AI Age: Maintaining Standards Despite Faster Iteration* [01:12:30] Prototyping vs. Production Code: The Superpower and the Danger* [01:16:50] Shifting Left with AI: Empowering Product Owners and Accelerating Feedback Loops* [01:19:40] Bootstrapping New Technologies: From Zero to One in Minutes Instead of Weeks* [01:23:15] Advice for Junior Engineers: Building Intuition in the Age of AI-Assisted Development ## Notable Quotes > "All of us are new to this experience. Nobody went to school back in the 80s and has been doing this for 40 years. We're all just running around, bumping into things and seeing what works for us." — Ryan Torvik > "An LLM is just a token generator. You stick an input in, and it returns an output, and it has no way of judging whether this is correct or valid or useful. It's just whatever it generated. So it's up to you to give it input data that will very likely result in useful output data." — Luca Ingianni > "Tests tell you how this is supposed to work. You can have it write the test first and then evaluate the test. Using tests helps communicate - just like you would to another person - no, it needs to function like this, it needs to have this functionality and behave in this way." — Ryan Torvik > "I find myself being even more aggressively biased towards test-driven development. While I'm reasonably lenient about the code that the LLM writes, I am very pedantic about the tests that I'm using. I will very thoroughly review them and really tweak them until they have the level of detail that I'm interested in." — Luca Ingianni > "It's really forcing me to be a better engineer by using the LLM. You have to go and do that system level understanding of the problem space before you actually ask the LLM to do something. This is what responsible people have been saying - this is how you do engineering." — Ryan Torvik > "I can use LLMs to jumpstart me or bootstrap me from zero to one. Once there's something on the screen that kind of works, I can usually then apply my ...