AI Engineer

The journey

1. What you learn

   1. How language models work and where they fail
   2. Prompt design, retrieval, and tool use
   3. Evaluating and shipping model-backed features
   4. Cost, latency, and safety trade-offs in production

2. What you will understand

   1. Tokens, context windows, and embeddings
   2. Retrieval-augmented generation
   3. Evaluation and regression testing for non-deterministic systems
   4. Guardrails and prompt-injection defenses

3. What you will build

   1. A question-answering assistant grounded in your own documents
   2. An evaluation suite that catches regressions across model versions
4. 1. The proof you build

      Evidence that you can design, evaluate, and ship a model-backed feature with attention to correctness, cost, and safety.

      See how proof works