If you’ve felt the pressure to “go agentic” or bolt RAG onto everything, this conversation is a deep breath and a better plan. We dig into the real decision points behind modern AI: when a clean prompt solves the problem, when retrieval is worth the effort, and when an agentic system adds cost without adding value. Along the way, we call out how vibe coding accelerates learning but can sabotage maintainability when teams don’t understand the code they ship.
We get practical about data. More isn’t better—better is better. You’ll hear how RAG actually raises the bar for data hygiene, why outdated or messy documents produce confident wrong answers, and how to build retrieval steps that respect source structure and change cadence. From noisy transcripts to multilingual contexts, we map the preprocessing and governance moves that prevent hallucinations and keep answers grounded.
Then we unpack agentic AI as a network of specialists: models and tools with clear roles, routed by a coordinator that chooses the right path, including non-LLM components for math or structured queries. It’s powerful, but not a default. We weigh costs, reliability, and the risk of overengineering when classic ML, search, or a database would do. The through-line is human judgment: engineers stay in the driver’s seat, setting constraints, validating reasoning, and designing systems that can be supported over time.
If you care about building AI that lasts—clean prompts over cargo-cult pipelines, data quality over dashboards, agents where they fit—this one’s for you. Subscribe, share with a teammate who needs a sanity check, and leave a review with your biggest AI misconception so we can tackle it next.