What “advanced” actually means here
Calling an LLM and printing the reply is a beginner exercise. Advanced AI engineering is about everything that happens around the model call: measuring quality, controlling cost and latency, recovering from failures, and coordinating multiple model calls into a reliable system. The ten projects below are chosen because each one forces you to build a piece of production infrastructure that demos quietly skip. Build them in order — later projects assume the muscles the earlier ones develop.
The ten projects
1. An LLM eval harness. Build a framework that runs a fixed test set through a prompt and scores outputs automatically — exact match, regex, an LLM-as-judge rubric, and latency. Store every run so you can diff two prompt versions. This is the foundation; nothing else is measurable without it.
2. A prompt regression test suite. Wire your eval harness into CI so a pull request that lowers the pass rate fails the build. Treat prompts like code with golden outputs and snapshot diffs.
3. A self-healing RAG pipeline. Build retrieval that detects when the top-k chunks are irrelevant (low similarity, no answer found) and automatically re-queries with a rewritten question or widens the search. Log every miss so you can improve chunking.
4. A multi-agent research system. A planner agent decomposes a question into sub-questions, worker agents answer each from the web or a corpus, and a synthesiser merges the findings with citations. The hard part is bounding cost and stopping loops.
5. A latency-optimised inference server. Wrap a model behind an API that streams tokens, batches concurrent requests, caches identical prompts, and falls back to a smaller model under load. Measure P50/P95 before and after each optimisation.
6. A semantic cache. Cache responses keyed by the embedding of the prompt, not its exact text, so paraphrased questions hit the cache. Measure hit rate and the cost saved.
7. A structured-output extraction service. Force the model to return schema-valid JSON for every input, with automatic re-prompting on validation failure and a dead-letter queue for inputs it can never satisfy.
8. A guardrails layer. Build input/output filters that detect prompt injection, PII, and off-topic requests, with an eval set proving the false- positive rate stays low.
9. A fine-tuning + eval loop. Fine-tune a small open model on a narrow task, then prove with your eval harness that it beats the base model on that task while staying cheaper and faster.
10. An agent with durable state. Build an agent whose long-running task survives a process crash by persisting each step, so it resumes mid-task rather than restarting. This is the difference between a script and a service.
How to get the most out of them
For every project, define the success metric before you write code: a target P95 latency, an eval pass rate, a cost ceiling per query. Then keep a changelog of what you tried and what it did to the number — that record is more valuable than the code itself. Resist the urge to chase a bigger model when a better prompt, a cache, or a retrieval fix would do. The engineers who stand out are the ones who can say exactly why their system is fast, cheap, and reliable — not just that it works once on a clean input.