Quick take
Everyone wants AI in their product now. The model is the easy part. The hard part is making a probabilistic system behave like reliable software. Ship the smallest useful thing, wrap it in guardrails, and instrument everything.
ChatGPT dropped in late 2022 and suddenly every product manager I know had “add AI” at the top of their backlog. Fair enough. The technology is genuinely impressive. But I’ve been in enough rooms now – calls with telecom companies, internal discussions at a financial infrastructure company – to notice a pattern. Teams that treat AI as a product feature ship well. Teams that treat it as magic ship demos.
The gap isn’t model quality. The gap is engineering discipline.
The Demo Trap
Here is what happens. Someone builds a demo over a weekend. It works beautifully with curated inputs. Leadership gets excited. “Ship it.” Then real users show up with messy prompts, edge cases, and the kind of creative abuse that no one anticipated.
I’ve seen this movie before, just with different technology. Microservices had the same arc. Kubernetes had the same arc. The technology works. The problem is people skip the boring parts.
What Production Actually Demands
After watching several teams go through this in January 2023, the requirements are depressingly consistent:
Reliability under partial failure. Your model provider will have outages. Your requests will time out. You need retries with backoff, circuit breakers, and a fallback that doesn’t leave users staring at a spinner. Standard distributed systems stuff, but teams forget it applies here too.
Quality gates that are explicit. If you expect JSON back from the model, validate it like an API contract. Reject malformed responses. This isn’t optional. I’ve watched teams debug production issues for hours because they trusted the model to always return valid structured data. It won’t.
Cost awareness from day one. Usage grows fast once the feature is visible. I mean really fast. Make the cost model visible to product owners early, because the conversation about “we need to turn this off, the bill is insane” isn’t fun to have retroactively.
Observability. If you aren’t measuring latency distributions, error rates, and cost per request, you’re flying blind. And you’ll discover problems from user complaints instead of dashboards.
The Architecture That Survives
The model call itself is rarely where teams struggle. The surrounding lifecycle is the hard part:
request -> normalize -> cache check -> model call with timeout
-> validate -> accept or fallback -> log and meter
Three patterns keep showing up in teams that ship successfully:
- Separate sync from async. User-facing calls need streaming and tight timeouts. Background processing can be batched and retried. Don’t mix them.
- Cache aggressively. Many inputs are repetitive. A warm cache cuts cost and latency dramatically.
- Degrade gracefully. When the model fails, return something useful. “No result” is better than a hallucinated answer that looks confident.
Validation Isn’t Optional
I want to stress this because I keep seeing it skipped. If a response might contain sensitive data, run detection and redaction before it hits users or logs. If the output feeds into another system, validate the schema. For anything high-stakes, add a human review step.
The model doesn’t know what’s sensitive. That’s your job.
Prompts Are Code. Version Them.
Treat prompts, templates, and model settings as versioned assets. Roll out changes gradually. Measure the impact. Performance drifts with provider updates and data shifts, and if you aren’t tracking versions, you won’t be able to tell why quality changed last Tuesday.
Set up alerts on error rate, latency regressions, and usage spikes. Those are your early warning system.
Start Small, Stay Honest
Pick one narrow use case with clear success criteria and an obvious fallback. Instrument everything. Learn from real traffic. Expand only after the behavior is stable.
AI in production isn’t magic. It’s engineering. The teams that respect that reality are the ones actually shipping.