How I Ship AI Features Fast as a Solo Developer

Shipping AI features fast as a solo developer means ruthlessly cutting scope and automating everything else.

As a solo founder building products at Anjeer Labs, I’ve learned that speed isn't about writing code faster; it's about making fewer decisions and eliminating entire categories of work. The goal is to go from idea to a working, user-facing AI feature in days, not weeks. Here’s the system I use.

Start with the API, not the model

Your first instinct might be to fine-tune an open-source model. Don't. For 90% of features, a hosted API is the correct starting point. I default to the OpenAI API for its reliability and speed. The cognitive load of managing infrastructure, even with tools like Hugging Face, will kill your momentum.

Define your feature's core interaction as a simple prompt and a completion. Get that working in an hour. For example, a text summarizer is just a call to gpt-3.5-turbo.

// This is your entire v1 "AI engine"
async function generateSummary(text: string): Promise<string> {
  const response = await openai.chat.completions.create({
    model: "gpt-3.5-turbo",
    messages: [
      { role: "system", content: "You are a helpful assistant that summarizes text concisely." },
      { role: "user", content: `Summarize this: ${text}` }
    ],
  });
  return response.choices[0].message.content;
}

Only consider a custom model if the API cost becomes prohibitive at scale or you have a truly unique data requirement. By then, you'll have validated the feature.

Build a bulletproof prompt pipeline

Prompt engineering is a misnomer; it's prompt debugging. You will iterate constantly. To ship fast, you need a system to test prompts without redeploying code. I build a simple admin panel—often just a protected page on suhailroushan.com—that lets me tweak system prompts and see outputs in real-time.

More critically, I structure all prompts using a templating engine like Handlebars. This separates logic from content and allows for variables.

import Handlebars from 'handlebars';

const promptTemplate = Handlebars.compile(`
You are a {{tone}} writing assistant.
The user is writing about: {{topic}}.
Please help them improve this text:

{{userText}}
`);

const finalPrompt = promptTemplate({
  tone: 'professional',
  topic: 'project management',
  userText: userInput
});

This pipeline lets me A/B test prompts on real user data instantly, which is the single biggest lever for improving AI feature quality.

Do you really need vector search?

For retrieval-augmented generation (RAG), everyone jumps to vector databases. For v1, you probably don't need one. I often start with a simple full-text search using PostgreSQL. If my documents are short or I'm searching over a known, small set of items (like a help center), pg_trgm or full-text search is faster to implement and often good enough.

I only reach for a vector database like Pinecone or pgvector when semantic search is non-negotiable—for example, finding conceptually similar support tickets where keyword matching fails. Adding that complexity from day one is a trap.

Implement mandatory user feedback loops

An AI feature shipped is an experiment launched. Without feedback, you're blind. I bake feedback collection directly into the UI. Every AI-generated output gets a simple "Was this helpful?" thumbs up/down. This data is gold.

I log the prompt, the output, the model used, and the user's feedback. This log isn't just for analytics; it's your training dataset for prompt iteration and, potentially, for fine-tuning later. This system runs on autopilot and informs every improvement.

Automate your deployment and monitoring

Your deployment should be a single command. I use GitHub Actions to deploy to Vercel or a similar platform. More importantly, I set up basic AI-specific monitoring from day one: token usage per request, latency, and error rates for API calls. I also alert on sudden cost spikes or increased error rates from my provider. This safety net lets you ship with confidence.

The honest takeaway: Speed comes from constraining your choices to a single, well-trodden path until a concrete problem forces you off it.