By Louis-Francois Bouchard (X, YouTube, other AI resources)
Goal: give you decision-ready references for the most common AI engineering problems. Open a cheatsheet, find your situation in the table, and follow the recommendation.
Model and tool preferences are a snapshot from August 2026. Treat them as tested starting points, then validate them on your own workload.
| Cheatsheet | What you get |
|---|---|
| AI Engineering Playbook | Pick the right AI technique, model, effort level, modality workflow, prompting strategy, RAG setup, memory pattern, eval method, and production config. |
| Agent Architecture and Operations Guide | Decide between workflow, single agent, and multi-agent, then operate it with curated context, evidence stores, phased review, portable skills, feedback loops, and safe scheduling. |
| Anti-Slop AI Writing Guide | Produce grounded, human-sounding writing with a 7-section prompt, checkable anti-slop rules, the current Towards AI long-form starting point, evidence-first review, one targeted rewrite, and platform checks. |
- Open the cheatsheet relevant to your problem.
- Find your situation in the decision tables.
- Follow the recommended approach.
For the Anti-Slop guide, assemble the sources first, fill in the complete template, finish the draft, and then run the separate evidence-first review before your human edit.
These cheatsheets come from the Towards AI courses. They cover the same frameworks in more depth, with full lessons, code, and hands-on projects.
- Full Stack AI Engineering - Prompting to production-grade RAG system. Position yourself for the roles, projects, and opportunities that AI is creating right now.
- AI for Business Professionals - Learn to get real benefits from LLMs for valuable tasks from experts that build AI for a living.
- Agentic AI Engineering - Your Path to Agentic AI for Production.
- Free Agents Webinar - Workflows vs. agents deep-dive (YouTube).