production AI
10 posts
A meditation app shipped a switch statement as AI
Whether a product 'really uses AI' is unanswerable and beside the point. What predicts reliability is system design: validated inputs, constrained outputs, fallbacks.
The demo passed. Two weeks later, the queue filled.
Prompt engineering treats AI as magic. Reliable LLM systems come from validation, retries, fallbacks, and monitoring - not better wording.
![The refund letter addressed to Dear [Name]](/images/posts/the-refund-letter-addressed-to-dear-name.png)
The refund letter addressed to Dear [Name]
Why ChatGPT's first output is a draft, not a deliverable, and what production AI systems actually require beyond the prompt.
arXiv just raised the bar
arXiv's one-year ban on unchecked LLM errors signals a shift: validation pipelines, not better prompts, now define competent AI systems.
Complexity theory never said that
Complexity theory does not prove human-level ML is impossible. Here is what the theorems actually say and how to design AI systems around real constraints.
AI costs more than humans
Nvidia says AI costs more than human workers. The real issue is architecture, not compute price. Here is how to fix the unit economics.
How Production Systems Actually Work With LLMs-Not Which Model You Choose
Production-grade AI systems don't depend on choosing between Claude and ChatGPT. They rely on consistent engineering: input sanitization, output validation, fallback logic, and structured pipelines-regardless of the underlying LLM.
Why 'AI Agent in Seconds' Platforms Fail in Production
Most 'AI agent in seconds' platforms sacrifice reliability for speed. Real production use demands validation, state persistence, and observability-features most no-code tools lack. This post explains why quick deployments fail at scale and how to build systems that actually endure.
Why LLM Outputs Fail in Production-and How to Fix It
Non-deterministic LLM behavior leads to silent failures in production when outputs aren't validated. Learn how structured validation prevents cascading errors in real-world systems.
Why AI Systems Fail in Production - And How to Fix It
AI systems fail in production not because of poor models, but due to uncontrolled inputs and unchecked outputs. Learn how deterministic validation and structured pipelines ensure real-world reliability.