AI Prompts Are Code… Treat Them Like It
In production, a prompt isn’t “just a string”. It’s business logic. Track it, test it, and version it like code, or you’ll ship invisible bugs.
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Why So Many AI Pilots Fail… and How to Make Yours Succeed
Most AI pilots look great in a demo but quietly collapse before reaching production. New research shows that nearly half of AI initiatives are abandoned, and even fewer ever deliver real ROI. The problem isn’t the technology; it’s weak foundations, unclear ROI, and poor adoption strategies. This article explores why so many AI projects fail, and what business leaders can do differently to turn demos into production-ready solutions that deliver real value.
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Guardrails Are More Than Filters
Guardrails aren’t just filters. They’re active safety systems. Without them, your production AI risks saying things it shouldn’t, damaging trust and reputation.
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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.
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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.
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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.
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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.
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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.
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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.
