Deep explorations of agentic AI, RAG architectures, and production engineering.
64 articles
Prompt caching can cut input cost by up to 90%, but only if you structure prompts so the cache actually hits. Here is how it works and when it pays off.
What state, checkpoints, and threads actually are in LangGraph, how to pick a checkpointer, and how persistence powers memory, resume, and human-in-the-loop.
A practical playbook for making LLM apps cheaper and faster without hurting answers: where the cost and latency actually go, and the levers that work.
A practical guide to keeping production agents safe: where to put input and output rails, how to contain tools, and why prompt injection needs layers, not a fix.
A full walkthrough of streaming LLM output from the provider, through your backend, into a React UI: SSE, backpressure, cancellation, and the gotchas.
The field moves faster than any course can. So I learn production AI by gutting and rebuilding the open-source projects that production teams actually run. Here is the path.
A deep, hands-on tour of Vercel's Eve framework for durable agents: the filesystem-first model, every core capability, a complete TypeScript research agent, and an honest comparison with LangChain's Deep Agents.
A clear split between an agent's working memory and its cross-session memory, with LangGraph code for checkpointers, stores, and the semantic/episodic/procedural breakdown.
Structured outputs guarantee the model's response matches your JSON schema. Here is how it works under the hood, how it relates to function calling, and the patterns that hold up in production.