Basic or above
Agent Deployment
Every deployment path the AISL workshops have actually taken an agent through — FastAPI on Railway, AWS Lambda, Cloudflare Workers, Vercel, and vLLM on rented GPUs.
Topics
AISL Wiki
Topic guides built from every AISL course, workshop, and article.
The wiki turns the AISL material — courses, workshops, articles — into topic-oriented guides. Start with the role, follow the roadmap, or jump straight to a topic. Start here AI Engineering — the role…
Basic or above required
Upgrade to Basic to read this topic guide
Get full access to this content and more with a membership.
All topics
Basic or above
Agents
What an agent is — a loop around tool calls — and how the AISL material builds, extends, and hardens them across frameworks, protocols, and modalities.
Basic or above
AI Coding Tools
The tool landscape — chat apps, IDE assistants, project bootstrappers, agents — and how AISL members actually work with them day to day.
Basic or above
AI Engineering
What an AI engineer does, how the role differs from ML engineering and data science, and how the AISL courses, workshops, and articles fit together.
Basic or above
AI Engineering Career
Portfolio projects, CV tailoring, personal brand, and interviews — the career mechanics the AISL workshops and articles cover, aimed at AI engineering roles.
Basic or above
AI Engineering Roadmap
A learning route through the AISL material: what to learn first, which course or workshop covers it, and what to build along the way.
Basic or above
Building Coding Agents
How to build the agents that write code: tool sets, templates, skills, slash commands, and the remote environments they run in.
Basic or above
Evaluation and Monitoring
Testing agents, evaluating them with judges and synthetic data, monitoring them in production, and paying less for all of it.
Basic or above
Guardrails
Input and output guardrails for AI agents: the SDK-native way, the framework-agnostic way, and what streaming and cancellation add.
Basic or above
Interview Prep
What AI engineering interviews actually test — the hiring-manager walkthrough, the theory question bank, and the AISL material that doubles as rehearsal.
Basic or above
LLM APIs and Providers
One API shape, many providers — OpenAI, Anthropic, Gemini, Bedrock, Groq, Grok, Z.AI, Ollama — and the tool-calling layer that makes them interchangeable.
Basic or above
Model Context Protocol
MCP — moving agent tools out of the notebook and behind a server any client can reach: FastMCP, stdio and SSE transports, Cursor and VS Code.
Basic or above
Portfolio Projects
The AISL showcase projects — ten builds from reference to-do agent to hospital platform — read as evidence for the portfolio framework.
Basic or above
Prompt Engineering
Prompts as the programming interface of AI work: system prompts, instruction strengthening, and the point where a prompt hands off to a schema.
Basic or above
Python for AI
The Python foundation the rest of the AISL material assumes: the Python for AI Engineering course, built around one real project.
Basic or above
RAG
Retrieval-augmented generation across the AISL material: the classic pipeline, where it breaks, and how agentic search fixes it.
Basic or above
Structured Output
Getting JSON instead of prose from an LLM — Pydantic schemas, where prompting stops being enough, and streaming structured output to the UI.
Basic or above
Vector Search
Search from first principles — TF-IDF, boosting, embeddings — and the small-technology versions the AISL workshops actually deploy: SQLite, Turso, Cloudflare Vectorize.