Building Your First AI Feature: A Realistic Timeline for a Solo Developer
The demo takes an afternoon. Making it reliable enough to ship takes considerably longer — here's where the time actually goes.
Wiring an API call into a working prototype of an AI feature is genuinely fast now — a solo developer can have something that looks impressive running in an afternoon. What tends to catch people off guard is how much further there is to go between "this works in my demo" and "this is something I'd actually ship," and where that extra time goes.
The prototype phase is the fast part: picking an approach, writing a first prompt, and getting a response back that looks roughly right. If your feature needs to answer questions grounded in your own data rather than general knowledge, this is also where you'd introduce embeddings and a vector database, covered in more depth in this site's explainer on that topic — expect this piece to add real time on its own, since it means chunking your content, generating and storing embeddings, and getting retrieval to actually surface the right passages.
The next stretch is evaluation, and it's where solo developers most often underestimate the work. A handful of examples that look good isn't the same as knowing how the feature performs across the range of inputs real users will actually send it. Building even a small, informal set of test cases — including ones designed to break it — and checking outputs against them is what turns "seems to work" into "I actually know how well this works," and it takes real, deliberate time rather than happening automatically as a side effect of building the feature.
After that comes hardening: handling cases where the API call fails or times out, deciding what happens when a response comes back malformed or unhelpful, adding rate limiting so a single user or a bug can't run up an unexpectedly large bill, and putting some kind of monitoring in place so you find out about problems before your users tell you. None of this is glamorous, and none of it shows up in a demo, but it's usually a larger share of total effort than the core feature itself.
For a solo developer working part-time, it's realistic to expect a working prototype within days, a version you'd trust with real users within a few weeks, and ongoing refinement after that as real usage surfaces edge cases you didn't anticipate. The exact timeline depends heavily on how much the feature depends on your own data versus general model knowledge, and how costly a wrong or bad answer would be to the people using it — a feature that just drafts a first pass of text can ship faster and looser than one making decisions that affect someone's money or data.