Production LLM Systems

Beyond the Quickstart: Authentication, Error Handling, and Cost Management

Your prompt travels through 7 infrastructure layers before a single token comes back. A plain-language walkthrough of API gateways, tokenization, prefill, decode, post-processing, billing, and the network physics underneath.

What 100+ Production Prompts Taught Me About Reliability

Context size is not the same as attention behavior. A practical guide for LLM architecture, RAG design, and long-context system trade-offs.

RLMs solve the context window problem by letting AI write code to explore information. The result? Tasks going from 0% to 91% success. Here's how it works and when to use it.

AI doesn't create garbage; it recycles your mess at warp speed. How bad data poisons AI at the training and prompting stages, and what you can do about it.

How RAG systems and context engineering can poison your AI, plus the governance layer and action plan to fix data quality across your entire pipeline.

AI doesn't fail loudly. It fails gradually, convincingly, and at scale. The failure modes that quietly wreck production systems before anyone notices.