0.3 AI Full-Stack Capability Map

Read the picture first. The course is one engineering path:

tools→Python→data→models→LLM→RAG→Agent→specialization/runtime delivery

You do not need every detail now. Just remember:

If the problem is	Go back to
running code	tools and Python
messy inputs	data
unreliable answers	evaluation and RAG
uncontrolled actions	Agent traces and permissions

The Seven Layers

Layer	Course chapters	First visible evidence	Deeper question
Tools	1	A reproducible project folder and Git history	Can another person rerun it?
Python	2	Small scripts with clear inputs and outputs	Is the code readable, typed, and testable?
Data	3	Clean tables, charts, and notes	Do you know where the data is wrong or biased?
Models	4-6	Trained or inspected model experiments	What metric would change your decision?
LLM	7	Prompt, tokens, embeddings, Transformer intuition	Which behavior comes from data, decoding, or context?
RAG	8	Retrieval trace and answer evaluation	Did the answer use the right evidence?
Agent	9	Tool traces, permissions, memory notes, deployment notes	What can fail when users, files, and actions are real?
Specialization / runtime delivery	10-13 and electives	Vision/NLP/multimodal/open-source LLM demos, exported assets, deployment notes	Which domain and runtime constraints change the product decision?

The course is not a pile of topics. It is a debugging stack and a portfolio stack. When an AI application behaves badly, the cause may live several layers below the feature you are looking at. When a reviewer asks what you built, your evidence should show which layers you controlled.

One Project Thread, Many Layers

A strong career-transition project can start as one small assistant or automation and become more credible chapter by chapter.

Layer	Portfolio evidence to add
Tools	Repository, README command, screenshots, and clean file layout
Python	CLI or script with visible inputs, outputs, errors, and tests
Data	Sample dataset, cleaning notes, charts, and edge cases
Models	Baseline, metric table, comparison, and failure samples
LLM	Prompt variants, structured output, token/cost notes, and limitations
RAG	Documents, chunks, retrieval trace, citation check, and answer evaluation
Agent	Tool permission boundary, action trace, memory rule, and rollback note
Specialization / runtime delivery	Vision, NLP, multimodal, open-source LLM runtime, deployment, or product-specific review evidence

Main Line And Expansion Tracks

Use Chapters 1-9 as the default main line. After Chapter 9, you should be able to build a small LLM/RAG/Agent project with evidence, logs, and a safety boundary.

Then choose Chapters 10-13 by product need:

Need	Choose	Why
Images, cameras, OCR, detection, segmentation	Chapter 10 Computer Vision	The output is visual: labels, boxes, masks, text, or video events
Text labels, extraction, summaries, linguistic evaluation	Chapter 11 NLP	The output is a text task with labels, fields, spans, or generated text
Images, PDFs, audio, video, creative assets, multimodal RAG	Chapter 12 Multimodal/AIGC	The workflow mixes modalities and needs source, prompt, review, and export records
Open-source model hosting, private deployment, runtime ownership	Chapter 13 Open-Source LLM Deployment	The project must control model files, serving API, licenses, cost, and fine-tuning decisions
Deployment, advanced Python, classic ML depth	Electives	The main project needs a specific engineering or algorithmic side skill

How To Use The Map

Before starting a project, mark the highest-risk layer. For example, a PDF question-answering app usually fails first in data cleaning and retrieval, not in the chat UI. An automation agent usually fails first in tool permissions, state, and evaluation, not in the prompt wording.

During each chapter, keep one artifact that proves the layer works. Screenshots are useful, but logs, README commands, small datasets, metric tables, and failure notes are stronger because they help you debug later.

Optional background: if you want the history behind these layers, skim the 15-stage AI development map.

Next, plan how you will pace the main route.

Pass Check

You pass this map when you can mark your current layer, the highest-risk next layer, and one portfolio artifact that will prove progress.

Evidence to Keep

Keep this page’s proof of learning as a small evidence card:

Capability Map

tools, Python, data, math, ML, DL, LLM, RAG, Agent, specialization, and runtime links

Project Thread

one assistant, automation, analysis, or multimodal project idea

Current Position

what you already know and what you will postpone

Next Step

one concrete chapter or workshop to start next

Risk Check

learning everything at once, skipping evidence, or losing the main route

Expected Output

a marked personal course map with one project thread and one immediate action