Move from demo to working system with validation, routing, caching, batching, request lifecycles, and practical application workflows.
Tutorials
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The invoice climbs the moment your prototype starts running regularly. The same system prompt is reprocessed from scratch on every call. The same FAQ is…
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Here's a scenario I see constantly: an application sends every request to one frontier model. Summarize this email in two sentences? Frontier model.…
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Routing is a bet on a classification. The break-even tells you the odds before you place it.
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Your fallback chain looks correct in review. Then the primary stalls at 900ms, the backup is rate-limited, and nobody can say whether the request…
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You asked the model for JSON. You defined the schema, listed the required fields, and specified the exact types. The response came back wrapped in markdown…
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Most developers don't fail with AI coding assistants because the code is wrong. They fail because the code looks right.
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A single fast response tells you almost nothing about whether your LLM system can handle real load. The request that feels snappy in isolation is often the…
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That single distinction separates a demo from a system. When you chat with an LLM directly, you are the pipeline: you judge the output, check the facts,…
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When builders hit a multi-path LLM application, many reach for an agent. That instinct costs them. The real mechanism for most of these systems is a…
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An LLM that reads a document and returns JSON is not performing a database import. It is performing an act of reading—and reading, even by a very capable…
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The function looks clean. It reads well. It handles the obvious case. Then it returns the wrong number for "1h30m", and you find out three days later.
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A model returns clean JSON. The pipeline writes it to a table. One wrong field quietly poisons everything downstream.
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The cheapest route on the pricing page is often the most expensive one in production.
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A cache hit returns in 40 milliseconds, reads fluently, and is wrong. Nothing in the response text tells you that. Only the metadata sitting beside it can.
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Throughput climbs, the dashboard looks healthy, and p95 quietly doubles. Here is the arithmetic that explains why.
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The four-line API call is not the hard part. The hard part is the key, the bill, and the error message you cannot read. So let's get all three out of the…
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A 60% hit rate does not mean a 60% discount. It means you have one number and three missing ones.
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Static batch math tells you what happens when the queue is already full. Real traffic does not cooperate. Requests arrive in clumps, then nothing arrives…
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A validator can tell you the shape of an answer. It cannot tell you whether the answer is true.
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At 9:05 a.m., your dashboard says you are fine. Your daily average sits comfortably inside the published limits. Then the 429s start.
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A retry loop that looks correct in code review can still double your request volume, blow past a deadline, or replay a non-idempotent call. You cannot see…
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A schema validator turns green, the response parses, and the builder ships it. The answer is still wrong. The check passed because it was never asked to…
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The weights fit. The model loads. Then the second concurrent request arrives, and the server refuses to admit it.
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A builder doubles the candidate count, watches the benchmark number climb, ships it, and then discovers that p95 latency and per-request cost moved in ways…
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Your validation step passes. Retrieval returns documents. The model answers. Verification approves. Four green checkmarks, and the feature still fails on…
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