Prerequisites
What to know before starting, organized into three tiers, each broken into topics and further into
specific sub-skills with a self-check question and one curated resource apiece — a short video
where a good one exists, otherwise a course, book, or official guide. This section expands on
Prerequisite_Knowledge.md, which has the full
narrative version (why each topic matters, chapter-by-chapter) if you want more context than the
condensed pages here give.
On this page
The three tiers
Browse a tier below, via the sidebar, or jump straight to a topic — each tier page has its own table of contents.
| Tier | Meaning | Topics |
|---|---|---|
| Essential | You must understand these to benefit from the book | LLM Fundamentals, Python, REST APIs & API Design, Command Line & Shell, Docker, Machine Learning Fundamentals |
| Recommended | You can succeed without them, but expect to look things up often | Software Architecture Patterns, Database Fundamentals, Kubernetes, NLP Basics |
| Beneficial | Accelerate learning and deepen understanding, not required | Distributed Systems, GPU/CUDA Basics, Prompt Engineering, Async Python, CI/CD |
One change from the narrative document: “RESTful APIs and HTTP Fundamentals” and “API Design and REST Principles” covered mostly the same ground at two depths, so they’re merged into one Essential topic — a “core” group and a “going deeper” group — rather than duplicated across two tiers.
Quick self-assessment
You should be able to answer yes to all five before starting — each links to its full subtopic if you can’t:
- I understand how LLMs work: tokens, context windows, prompting (Essential > LLM Fundamentals)
- I can write and debug intermediate Python (Essential > Python)
- I understand REST APIs and HTTP basics (Essential > REST APIs, HTTP, and API design)
- I can use command-line interfaces and write shell scripts (Essential > Command Line)
- I understand Docker containers and can build images (Essential > Docker)
If you cannot check at least four of the five, plan two to three weeks of remediation before starting.
Preparation paths
| Path | Duration | Effort | Covers |
|---|---|---|---|
| Minimal | 2–3 weeks | 25–35 hours | Essential only, enough to start Parts 1–2 |
| Recommended | 4–5 weeks | 40–60 hours | Essential plus Recommended |
| Comprehensive | 6–8 weeks | 60–90 hours | Essential, Recommended, and Beneficial |
Background-specific guidance (academic students, software engineers, ML practitioners, career
changers) is in the closing section of
Prerequisite_Knowledge.md.