Trails
AI engineering trails for software engineers
Six trails of 12 to 14 lessons that form one course, in order, with no lesson repeated. Each mixes fundamentals, smells, refactorings, and patterns, and ends in a real scenario. Checks along the way show what you understood, and the certificate is ready to download or post on LinkedIn.
- 01Start hereLLM foundations for engineersStart here. What changes when a model becomes part of your system: prompts as interfaces, structured output, tools, and when a fixed workflow beats an agent.Final project: Store support on WhatsApp
- 02Evals and reliabilityHow to know an LLM feature works before your users tell you: evals, golden datasets, structured contracts, and deterministic work moved back into code.Final project: Invoices into the ERP
- 03RAG and contextGive the model the right information at the right time: embeddings, retrieval, hybrid search, reranking, chunking, and long conversations that do not rot.Final project: Internal knowledge chat
- 04Building agentsGive the model autonomy without the classic accidents: loops with a budget, a small toolset, subagents, and multiple agents only when they pay off.Final project: Coding agent
- 05Securing AI systemsFor whoever answers for what the agent does with money, data, and customers: prompt injection, least privilege, human approval, and an audit trail.Final project: Finance assistant with approval
- 06Production: cost, latency, and operationsCheap, fast, and observable: tracing, parallel steps, caching, model cascades, a fallback when the provider goes down, and a stack without vendor lock-in.Final project: Deep research with citations