AI Models Change. Your App Must Too.
Models evolve constantly. If your app can’t adapt, it will break silently. Build for change from day one to keep your AI reliable in production.
-
-
The Real AI Work Is in the Edges
Your demo lives on the happy path. Production AI lives in the typos, slang, and weird edge cases. Handle them well, and you’ll keep user trust.
-
Measure AI Cost Metrics from the Beginning
AI costs can spiral quickly if you’re not tracking them. Learn how to log and monitor the spending tied to your LLM, workflow, and storage usage, so you can scale with confidence and stay within budget.
-
The Most Expensive Thing You Didn’t Build Into Your AI Demo
A demo only needs to work once to impress. Production AI needs to work every time for every user. That leap from “sometimes” to “always” is where reliability costs explode—and if you don’t design for it early, you’ll end up rebuilding under pressure.
-
Log LLM and Workflow Performance Metrics
Speed, reliability, and consistency are critical for production AI. Learn how to log response times, success rates, and throughput for your LLM and automation workflows, turning performance data into insights you can use to optimize and scale.
-
Run Your Logging Platform on a Separate Server
LLM-powered automations often log prompts, responses, and user metadata, but raw logs can expose sensitive info like emails and user IDs. This post shows how to build a simple Python logger that anonymizes data before writing it to disk, protecting privacy without sacrificing observability.
-
When you “Hit the Target” but Miss the Mark in AI
I’ve got a childhood memory that’s weirdly perfect for how businesses do AI. My brother and I were wandering in the woods with our BB guns, looking for a target. He pointed to a dark spot on a fallen log. So naturally, I aimed and shot precisely where he was pointing… which turned out to…
-
Rage Against the Machine Learning
I learned a lesson from ordering a t-shirt with a funny quote. Most AI demos look great, until someone asks, “Can we ship this?” That’s when the real work begins. In production, clever prompts aren’t enough. You need monitoring, access control, and systems that don’t fall apart under pressure. Because if you don’t build it right the first time, the AI will rage against you.
-
Anonymize Logs Before You Regret It
LLM-powered automations often log prompts, responses, and user metadata, but raw logs can expose sensitive info like emails and user IDs. This post shows how to build a simple Python logger that anonymizes data before writing it to disk, protecting privacy without sacrificing observability.
