LLM engineering
15 posts
Better AI isn't what separates winning deployments.
Stanford studied 51 AI deployments and found a 71 vs 40 productivity gap. The difference was pipeline design, not model choice.
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.
Managed Agents pricing is an architecture decision
Claude Managed Agents pricing isn't a cost center - it's an orchestration lever. Here's how to evaluate it against real total cost of ownership.
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.
Running Gemma 4 Locally via Codex CLI: What Actually Works in Practice
Running Gemma 4 locally via Codex CLI offers isolation but not guaranteed consistency. Real reliability comes from input validation, output schema checks, and disciplined system design-not the model alone.
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 Cloudflare CLI Automation Fails Without Verification
Cloudflare CLI automation fails without verification. This post explains why input validation, output checking, and idempotency are essential for reliable deployments-without speculative claims or exaggerated risks.
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.
Why Most AI Automation Fails in Practice - And How to Fix It
Most AI automation fails in practice because it redistributes effort rather than eliminating it. Learn how to build systems that actually reduce human workload through bounded domains, structured outputs, and rigorous pre-rollout validation.