Uploaded November 2025 | Updated September 2026, 2 weeks ago
Stop treating LLMs like magic black boxes or religiously following a framework. This video walks through practical lessons for shipping real-world AI products: identify the primitives (gateway, prompt, outputs, what you do with them), prefer smart automated RAG over over-engineered “memory” systems, and design around tool strengths. We cover product-first thinking (don’t default to chat), aligning metrics between science and UX, when vibe-evals are fine vs. when rigor matters, and why tight end-to-end feedback loops are your secret weapon. If you’re an ML engineer, product manager, or founder building LLM-powered apps, learn how co-designing with product teams, exposing the right controls for humans-in-the-loop, and treating components as composable primitives will save time and produce better UX.
#AI #ML #LLM #ProductDesign #PromptEngineering #RAG #NLP #MachineLearning #ProductManagement #AIProduct #HumanInTheLoop #StartupTech
Stop treating LLMs like magic black boxes or religiously following a framework. This video walks through practical lessons for shipping real-world AI products: identify the primitives (gateway, prompt, outputs, what you do with them), prefer smart automated RAG over over-engineered “memory” systems, and design around tool strengths. We cover product-first thinking (don’t default to chat), aligning metrics between science and UX, when vibe-evals are fine vs. when rigor matters, and why tight end-to-end feedback loops are your secret weapon. If you’re an ML engineer, product manager, or founder building LLM-powered apps, learn how co-designing with product teams, exposing the right controls for humans-in-the-loop, and treating components as composable primitives will save time and produce better UX.
#AI #ML #LLM #ProductDesign #PromptEngineering #RAG #NLP #MachineLearning #ProductManagement #AIProduct #HumanInTheLoop #StartupTech
