Description
The “prompt-and-pray” era is over — and that’s a good thing. In this episode, we break down why AI “magic” collapses under real production traffic (edge cases, hallucinations, messy inputs, and even infrastructure-level failures)… and what replaces it: actual AI engineering. Danny frames the shift with four architectural pillars that make LLM features shippable and reliable: - State orchestration (stop treating models like employees — they’re stateless CPUs) - Constraint generation (JSON forcing, schema-driven outputs, type-safe sampling) - Infrastructure reliability (retries, backoff, fallbacks — because inference can and will fail) - Regression testing & evals (measure prompts like code, break builds when quality drops) SITE https://www.programmingpodcast.com/ Stay in Touch: 📧 Have questions for the show? Or are you a business that wants to talk business? Email us at dannyandleonspodcast@gmail.com! Danny Thompson https://x.com/DThompsonDev / dthompsondev www.DThompsonDev.com Leon Noel https://x.com/leonnoel / leonnoel https://100devs.org/ 📧 Have questions for the show? Or are you a business that wants to talk business? Email us at dannyandleonspodcast@gmail.com! We also hit the reality of agent “throughput” vs human review bottlenecks (Phoenix Project vibes), why monolithic agents are a trap, and a listener question about networking + credibility after pitching an MVP that isn’t fully shipped yet. If you’re building AI features for real users — not demos — this is the blueprint. 00:00 — The “prompt-and-pray” era is over 02:49 — AI hype fades: guardrails + reality 06:34 — Deterministic software vs probabilistic models 07:29 — The 4 pillars of AI engineering (overview) 11:37 — Pillar 1: state orchestration (FSM, stateless models) 20:26 — Pillar 2: constraint generation (JSON, schemas, type safety) 28:28 — Pillar 3: infra reliability (retries, fallbacks, failures) 32:21 — Pillar 4: evals + regression testing (LLM-as-judge) 43:40 — Listener question: networking, MVP pressure, and credibility