LLM engineering
35 posts
Apple isn't competing with OpenAI
Apple's AI strategy is a silicon bet, not a model race. The real architecture question is where inference runs - and who controls the hardware lane.
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.
Agents Need Orchestration
Managed agents aren't plug-and-play. Real reliability comes from structured pipelines with validation, state tracking, and fallbacks-no exceptions.
Claude Code's System Prompt Is a Production AI Agent Blueprint
Claude Code's system prompt is a working engineering spec for production AI agents. Six concrete patterns for context isolation, tool selection, parallelism, error recovery, memory, and blast radius management.
The Real Architecture Behind Reliable AI Systems
Reliability in AI systems comes not from smarter models or autonomy, but from deterministic control, validation, and predictable failure recovery-patterns already proven in real-world production environments.