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From Prompt to Working Code in Seven Moves
A concrete, sit-down-and-do-it sequence for generating code with AI today, from setting up context to verifying the result before you ship it.
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Agency Script EditorialJanuary 31, 2024·7 min read
A concrete, sit-down-and-do-it sequence for generating code with AI today, from setting up context to verifying the result before you ship it.
A narrative account of one agency's first real AI API build: the client crisis that forced it, the decisions that shaped it, and the numbers that proved it worked.
Before you wire up a single model call, you need a business case that survives scrutiny. Here is how to quantify cost, payback, and the pitch that gets a yes.
Seven failure modes that come from misunderstanding AI statelessness, what each one costs you, and the fix. Most are invisible until they bite in production.
A narrative account of moving a document extraction system from intermittent failures to dependable structured output, with the decisions and the measured result.
A thesis-driven look at how structured output and JSON mode are evolving, from schema-native generation to typed tool calls, grounded in signals visible today.
Persistent memory feels like the obvious upgrade, but stateless designs win more often than teams expect. Here is how to choose between them on purpose.
A working pre-launch checklist for any AI API integration, grouped by cost, reliability, output safety, security, and measurement, with a one-line reason for every item.
A documented, repeatable workflow for structured output and JSON mode so the discipline survives hand-offs, scales across a team, and stops living in one engineer's head.
The failures that waste the most time are predictable. Here are seven recurring mistakes with AI code generation, why each happens, and the fix.
Structured output reduces parse failures, manual cleanup, and incident toil. Here is how to quantify the cost, the benefit, and the payback for a decision-maker.
A play-by-play operating guide for structured output and JSON mode, with triggers, owners, and sequencing so teams ship machine-readable model output that holds up.
Skip the theory paralysis. This is the fastest credible path from never having touched an AI API to a real, working result you can show someone.
Three broad approaches power AI coding tools, and they pull in different directions. Here are the axes that actually separate them and a rule for choosing.
The questions teams actually ask about structured output and JSON mode, answered plainly, with the trade-offs and gotchas that documentation tends to skip over.
A field-tested checklist for structured output covering schema design, enforcement, validation, retries, and monitoring, with a short reason behind each item.
Opinionated, battle-tested practices for designing memory around a stateless model, with the reasoning behind each. Skip the platitudes.
Memory systems fail quietly. These metrics surface stale recall, retrieval misses, and bloated context before your users notice the cracks.
The difference between an engineer who thrives with AI coding tools and one who fights them comes down to a handful of opinionated, hard-won habits.
A named five-stage framework, Contract, Retrieve, Apply, Filter, Track, for designing AI API integrations that stay reliable as they scale. Each stage with when to use it.
A short, credible path from nothing to a working structured-output call, including the prerequisites, a minimal schema, and the validation step you should not skip.
The headline number every AI coding vendor reports is the one that tells you the least. Here is how to instrument and read the signals that actually predict value.
You can make the calls. Now the edge cases, the latency tails, and the failure modes start mattering. Here is what separates a demo from a dependable system.
A named, reusable framework for structured output organized into seven stages, so you can design any extraction or classification pipeline from one mental model.
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