Getting Started with AI: A Practical Roadmap for Developers

If you already know how to code, you're closer to being productive with AI than most of the discourse around it suggests. You don't need a PhD, and you don't need to train a model from scratch. You need to understand how to use the tools that already exist, and build enough intuition to know when they'll actually help.

You Don't Need a PhD to Start

Most AI roles being hired for right now aren't research roles. They're engineering roles: integrating large language models into products, building retrieval pipelines, evaluating outputs, and handling the unglamorous plumbing that makes an AI feature reliable in production. That's a software engineering skill set with a new vocabulary layered on top.

A 90-Day Roadmap

1. Weeks 1–2: Learn how LLMs are actually used in practice — prompting, context windows, tool use, and structured output.
2. Weeks 3–6: Build a retrieval-augmented application: embed a real document set, wire it into a vector store, and connect it to a model through an API.
3. Weeks 7–10: Learn evaluation. Ship something, then figure out how to measure whether it's actually good — this is the step most self-taught people skip.
4. Weeks 11–13: Add guardrails: rate limiting, cost controls, and handling model failures gracefully. This is what separates a demo from a product.

Common Mistakes to Avoid

• Treating prompt engineering as the whole skill, instead of one part of a larger system
• Skipping evaluation because it's less fun than building features
• Ignoring cost and latency until they become a production emergency
• Building everything around one vendor's API with no fallback plan

The developers landing AI roles right now aren't the ones who memorized the most papers. They're the ones who shipped something real, broke it, and fixed it.