Building with AI
From demo to product
You send a message, you get a response, the end. Clean, simple, done ✅
But what happens when you want the model to remember the last three messages? When you need to stream a response word by word instead of waiting for the whole thing? When you want the model to call a function in your code, or keep looping until a piece of writing meets some quality bar? What do you do when a rate limit hits mid-request — and what does the user see when it does?
That space — between "hello world" and something you'd actually ship — is what this series is about.
What we're building
Throughout the series we will build Inkwell, a writing intelligence platform. Think of it as a minimal text editor where the assistant genuinely assists: rewriting a paragraph on request, critiquing a draft's tone, iterating on revisions, saving versions as it goes.
Along the way we'll cover the following topics:
Foundations — the basics done right. A single API call and everything in it: the request fields, the response structure, the things that will bite you if you ignore them. Then multi-turn conversations — how the messages array is the model's only memory, and how to use it for a revision thread.
Integration patterns — the primitives that turn a demo into a product. System prompts and writing modes. Streaming responses over SSE. Structured output — asking the model to return JSON and actually relying on its shape. Tool use, where Claude calls functions in your code to fetch and save drafts. And agentic loops, where Inkwell runs a polish cycle on its own until the draft clears a quality threshold.
Production concerns — the work that doesn't show up in tutorials. Prompt caching, which dramatically cuts costs once your context gets large. Latency instrumentation, because time-to-first-token is a user experience problem before it's a metrics problem. And failure handling — rate limits, retries, backoff, and what the user sees when things go wrong.
