AI Engineer Roadmap

Every AI feature starts with a reliable call to a model and parsing what comes back. Get this foundation right, including the messy non-JSON output models return, before building anything on top.

The prompt is the program. Writing classification and structured-task prompts that hold up on real inputs is the difference between a demo and a feature that works in production.

LLM calls are slow, flaky, and metered. Adding resilience and caching turns a fragile prototype into something you can actually run - and keeps the bill from exploding.

Embeddings turn meaning into math, and retrieval is what grounds a model in your data. Getting the embedding and search strategy right is the foundation every RAG system stands on.

Retrieval-augmented generation is the dominant pattern for putting LLMs on private data. Build one end to end - chunk, embed, retrieve, answer - because it is the project AI interviews center on.

You cannot improve what you cannot measure, and you should not pay for a frontier model on an easy request. Building an eval harness and routing requests to the right model is the senior AI-engineering skill set.