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From prompt to production — the missing AI learning curriculum

Most AI courses stop at "you can call an LLM". Production teams need ten more skills nobody bundles together. Here's the curriculum we built.

The gap

We mapped what an engineer actually needs to ship a real AI feature in 2026.

What courses cover well:

  • "What is an LLM?" — explained ten thousand ways
  • "Write your first prompt" — usually with the wrong defaults
  • "Train a tiny network on MNIST" — useful, but it's 2026

What courses skip:

  • Evaluation discipline. Vibes-driven prompt iteration kills products. You need a 50-case eval set, a scoring function, and the discipline to diff failures across versions.
  • Production patterns. Caching, retries, fallbacks, structured-output API mode, cost tiering. None of these are exotic — they're table stakes — and almost no curriculum teaches them.
  • The decision tree. When to prompt, when to RAG, when to fine-tune. Most teams burn weeks on the wrong layer.
  • Hybrid retrieval. Pure vector search misses error codes; pure keyword search misses paraphrases. Hybrid + reranker is the modern default. Few courses cover it.
  • Hosting & cost. vLLM vs TGI vs Triton. Self-host vs managed. €1500/month spend ramping into €15k if you don't tier models.
  • Observability. A probe set you re-run daily catches silent regressions before users do. Trail logs of (query, retrieved chunks, scores, output) make every "why did it answer that?" question debuggable.

What we did about it

We wrote six paths — 62 lessons, all interactive — that map to that gap:

  1. Prompt Engineering (12) — structure, constraints, few-shot, structured output, eval, production patterns.
  2. LLMs & Transformers (10) — tokens, attention, sampling, KV cache, model cards, reasoning models.
  3. RAG & Vector DBs (10) — chunking, hybrid search, rerankers, failure modes, eval, production.
  4. Fine-tuning (10) — when NOT to fine-tune, LoRA/QLoRA, dataset design, RLHF/DPO, hosting.
  5. Deployment & MLOps (10) — serving stacks, GPU choice, autoscaling, cost optimization, monitoring, rollouts, CI/CD.
  6. AI Engineering Foundations (10) — Python, vectors, gradients, loss, regularization, hand-written neural net.

Every path ends with a capstone that pulls the previous lessons into a real engineering decision. Every interactive challenge is graded — by rubric (prompt challenges), by execution (JS and Python code), or by exact match (multi-step ordering).

Why this works

Three pedagogical bets:

  1. You learn what you build. Reading is forgetting; running real code is remembering. Every lesson has a "now run it" beat.
  2. You learn what you debug. "When NOT to use X" is a lesson by itself. We trade vibes for engineering judgment.
  3. You learn for the credential. Each path connects to a CertQuests practice exam. Knowledge that doesn't survive a timed test is fragile knowledge.

Start free at [/paths](https://nextgenailearn.com/paths). The first lesson takes 8 minutes.

Try it.

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