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)
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Danny Thompson
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www.DThompsonDev.com
Leon Noel
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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
