📰 Dev.to · Jamie Cole

You shipped your LLM-powered feature. It worked perfectly in testing. Users loved the beta. Three...

Everyone says: use caching, use cheaper models, reduce token counts. Here is the one thing that actually cuts LLM costs by 40%. ## The Real Problem

After building LLM features for 18 months, here are the architecture patterns I have seen work at scale. And the two that consistently fail. ## Patte

Six months ago I built a SaaS tool using only free AI tools. No paid APIs, no expensive infrastructure. Here's what I learned about what actually work

The most common LLM production incident is silent quality degradation. Here is how to detect it before it breaks your users.

6 months of production data on LLM drift. Which models drift most, which tasks are affected, and how to detect it.

After 18 months of LLM integrations, these are the patterns that fail most often in production. Not theoretical failures — real incidents.

After a $3000 surprise bill, I built cost monitoring for every LLM call. Here's the exact architecture and what it cost me.

After 18 months of running LLMs in production, this is the exact monitoring setup I use and what it costs.

Everyone tests their prompts before launch. Nobody catches what happens when the model silently updates a week later.

A practical guide to adding automated LLM drift detection to your existing CI/CD workflow. Step-by-step with GitHub Actions.

After 300 automated drift checks across GPT-4o, Claude, and Gemini, here's exactly where models fail most often.

The exact tools I use daily for LLM development — not the popular ones, the ones that actually work.

The story of building an automated regression testing system for LLMs — and what it found when it watched GPT-4o for 30 days.

JSON mode, system prompts, and parsing tricks that make LLM output actually usable in production.

OpenAI retired GPT-5.1 on March 11 with automatic fallback to GPT-5.3/5.4. If your app calls gpt-5.1, it's now running a different model — silently. Here's exac

Unit tests don't catch LLM behavioral drift. Here's a practical CI/CD setup that detects format regressions, instruction compliance drift, and output changes be

Real data: same prompt, same model, two consecutive runs. Drift score 0.575. The cause: a trailing period. Here's the exact output diff and why it breaks produc

Real drift scores from DriftWatch on production-style prompts. Exact outputs shown. 0.575: trailing period dropped on sentiment classifier. 0.316: JSON whitespa

I had PromptFoo set up in CI. Evals passed on every deployment. The model still silently changed in...

OpenAI doesn't email you when GPT-4o changes. Anthropic doesn't either. You find out from users. Or...

Your CI/CD pipeline runs before every deploy. Your LLM prompt tests pass. You ship. Three days...

On February 10, 2026, OpenAI pushed a silent update to GPT-5.2 Instant. The release notes said it...

It started with a Slack message from a user: "Your summaries look weird." Not an error. Not a crash....