Orient through the chapter — name the four artifact families a non-coder can build with AI (custom assistants, single-file artifacts, full prototypes, end-to-end coding-agent workflows), see the skill-path map of the six chapters and how they relate, recognize the concrete capabilities at chapter end, and pick the recommended starting path (CLI-comfortable: 5.1.2; no-CLI starter: 5.1.3)

Get Claude Code on your Mac, learn how to brief it, give it a project memory with CLAUDE.md, capture repeated work as Skills, run a hands-on first project, and keep token spend honest — explained without jargon for non-coders

Understand AI agents from the ground up — distinguish them from chatbots and Claude Code, learn the agent loop, see how tools turn thinking into doing, recognize when an agent is the right shape, and have Claude Code build your first customer-research agent end-to-end

Stop re-pasting context every chat — package your prompts, files, and tone as reusable assistants on ChatGPT (Custom GPTs), Claude (Projects + Skills), and Gemini (Gems), with the four levers, sharing patterns, and when a saved prompt would do better

From a messy CSV to a single-file HTML dashboard you can email — paste data, brief the dashboard, iterate without breaking sections, share, and recognize where this approach fits (single-stakeholder snapshots) vs where it does not (live data, multi-user, regulated)

From a small-tool idea to a clickable demo at a URL — pick the right design-first surface (Lovable for working app, Claude Design for polished + hand-off-ready), brief in experience-language not implementation, iterate without breaking the prototype, and recognize the 80% wall that signals time to hand off to Claude Code for real implementation

Recognize the five predictable failure modes when AI coding agents stop cooperating (loop, context overflow, silent regression, phantom imports, configuration drift), apply the matching recovery move (interrupt, redirect, restart), and know when to escalate to a real developer instead of pushing the agent further

The map of the AI Engineering Foundations skill path — what each chapter teaches, how they fit together, and where to start

Write prompts that work like programs — structured, testable, and consistently effective

Master the art and science of curating optimal context for AI agents

Constrained decoding, schema engineering, and type-safe AI pipelines

Build the minimum viable RAG pipeline — chunk, embed, store, retrieve, augment, generate — in plain code

Take the retrieval backbone to production — embeddings, chunking strategies, hybrid retrieval, advanced patterns, and evaluation. Assumes RAG Foundations.

When to fine-tune vs RAG vs prompting — SFT, LoRA, dataset prep, and evaluation

Vision, document, PDF, and video processing — build with images and audio, not just text

Build high-quality training and eval datasets — synthetic data, labeling, and curation pipelines

Prompt caching, KV-cache, batching, speculative decoding, and token budgeting

Architect knowledge bases that AI agents can navigate, retrieve from, and act upon

Pick the right post-training algorithm — DPO, GRPO, Dr-GRPO, DAPO, KTO, GiGPO — without drowning in research papers

The map of this path — why running models locally is worth it, how the six chapters build on each other, and where to begin.

The model on your own machine is weaker than the frontier — and that trade is worth making more often than you'd think.

Four friendly front doors, one shared engine underneath — so the choice is about comfort, not capability.

One number decides whether a model runs on your machine — and one trick lets you bend it.

Your hardware already narrowed the field — now choose by the job and read the license before you commit.

Local models speak the same language as the cloud — so wiring one into your code is mostly changing a single line.

Outgrew your hardware? The upgrade is one number — grow the right kind of memory, and a whole model class opens up.