How teams ship real products with Claude, GPT, Gemini, RAG, agents, and evals.
Long-context models tempt you to stuff everything into the prompt. That's how you end up with slow, expensive, and weirdly dumb responses. Here's how we budget tokens in production.
Prompt caching looks like free money: stuff a giant system prompt once, pay pennies forever. The reality is messier. Here's when it actually saves you cost and latency, and when it quietly costs more than it saves.
JSON mode, strict schemas, and tool calls all promise reliable structured output from LLMs. They behave differently under load, failure, and schema drift. Here's how we pick between them.
Rerankers fix the recall-precision gap in RAG, but they cost latency and money. Here's when a cross-encoder actually pays off, and when you should tune retrieval instead.
Streaming tool calls feel like a free win until your agent state diverges, your UI flickers, and your retries double-charge users. Here's how to ship it without the footguns.
Picking one frontier model and praying is not a strategy. Here's how we route requests across Claude, GPT, and Gemini in production — by task shape, cost, and failure mode.
Semantic caching promises huge cost wins for LLM apps, but naive implementations leak wrong answers across users. Here's how we build cache layers that actually hold up.
Most LLM eval setups measure the wrong things and miss the regressions that actually break production. Here's how we build harnesses that catch silent failures before users do.
Pure vector search loses on acronyms, product codes, and rare names. Here's how we mix BM25 with embeddings to fix recall without rewriting the stack.
Agent loops quietly balloon context until a single task costs dollars instead of cents. Here's how we budget tokens per turn, per tool, and per task — with code you can paste into your own runner.
Prompt caching looks like free money until your hit rate collapses or your cached context goes stale. Here's how we decide when to turn it on, and when to leave it off.
JSON mode and structured outputs feel like a silver bullet until they aren't. Here's what actually breaks in production, and how we decide between strict schemas, function calling, and plain prompting.
Agents fail mid-run, networks flap, and tool calls retry. Here's how we design LLM agents that can be safely re-executed without double-charging cards, duplicating tickets, or corrupting state.
Streaming tool calls look great in demos and fall apart in production. Here's what we learned wiring them into agents that handle real traffic, retries, and partial failures.
We've shipped agents on all three frontier APIs in the last year. Here's how we actually decide which one runs in production — tool use, latency, cost ceilings, and the boring stuff that breaks at 3am.
RAG bills balloon fast once you ship to real users. Here's how we trim retrieval and generation spend by half or more without watching recall collapse.
Most teams write LLM evals once, watch them pass, and ship blind. Here's how we structure eval suites that fail loudly when a prompt tweak or model swap quietly breaks production.
