Deep explorations of agentic AI, RAG architectures, and production engineering.
64 articles
Examples are the most effective context you can add to a prompt, until they aren't. When few-shot beats zero-shot, how many to use, how to pick them, and where examples won't help.
A good prompt has up to four parts: instruction, context, input data, and output indicator. Here is what each does and the design habits that make them land.
Before you rewrite a prompt, check the dials around it. Temperature, top_p, max tokens, and the penalties each change your output in a specific way. Here is when to touch each.
An agent is a stateless model wrapped in a loop. Here is the ReAct pattern built from scratch: the message list as memory, the exact algorithm, and the rule that generate calls equal tool rounds plus one.
Git worktrees let you check out several branches into separate folders at once. They went from obscure to default because running coding agents in parallel needs isolated workspaces.
Anthropic's Claude Tag drops a shared, persistent agent into Slack channels. The interesting part is not the model, it is the move from one-off chats to a teammate that remembers.
Why model deprecation quietly breaks production apps, and a concrete workflow: one models constant file, a scheduled GitHub Action checking deprecations.info plus a liveness probe, and an alert.
A practical guide to LLM observability: the traces and metrics to log, the handful of alerts worth waking up for, and why traditional monitoring misses what matters.
How to choose how to structure an agent: the plain while loop, the explicit state machine, and the full graph. What each gives you, what each costs, and when to move up.