0.1 AI Engineering Mental Model¶

Role at Stage 0: Orientation¶

Build a mental model of AI products as systems around foundation models, not as isolated prompts. This part is one capability inside the stage. It should leave behind an artifact, measurements, and a short explanation of failure modes.

Explanation¶

This part has 4 sub-parts because the topic needs that many learning units to feel natural. Some stages have more parts and some have fewer; the structure follows the topic, not a fixed template.

Part Diagram¶

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flowchart LR
  P["<b>0.1</b><br/>AI Engineering Mental<br/>Model"]
  P --> S1["<b>0.1.1</b><br/>AI Engineering vs ML<br/>Engineering"]
  P --> S2["<b>0.1.2</b><br/>Foundation Model<br/>Product Stack"]
  P --> S3["<b>0.1.3</b><br/>Models as<br/>Probabilistic<br/>Components"]
  P --> S4["<b>0.1.4</b><br/>Adaptation Patterns"]
  P --> E["<b>Exam</b><br/>Part practice"]

Sub-Parts¶

Sub-part folder	What it explains
0.1.1 AI Engineering vs ML Engineering	AI Engineering vs ML Engineering is the working skill inside AI Engineering Mental Model that helps you build the stage artifact, A learning log, environment checklist, use-case decision memo, and first roadmap plan, while collecting enough evidence to trust the result.
0.1.2 Foundation Model Product Stack	Foundation Model Product Stack is the working skill inside AI Engineering Mental Model that helps you build the stage artifact, A learning log, environment checklist, use-case decision memo, and first roadmap plan, while collecting enough evidence to trust the result.
0.1.3 Models as Probabilistic Components	Models as Probabilistic Components is the working skill inside AI Engineering Mental Model that helps you build the stage artifact, A learning log, environment checklist, use-case decision memo, and first roadmap plan, while collecting enough evidence to trust the result.
0.1.4 Adaptation Patterns	Adaptation Patterns is the working skill inside AI Engineering Mental Model that helps you build the stage artifact, A learning log, environment checklist, use-case decision memo, and first roadmap plan, while collecting enough evidence to trust the result.

What a Person Who Masters This Part Can Do¶

Explain how AI Engineering Mental Model supports a learning log, environment checklist, use-case decision memo, and first roadmap plan..
Build and inspect this artifact: Draw an AI application stack for a simple assistant.
Measure progress with: Check owners, inputs, outputs, failure modes, and evaluation points.
Debug at least one failure mode before moving to the next part.

Build and Measure¶

Build: Draw an AI application stack for a simple assistant.

Measure: Check owners, inputs, outputs, failure modes, and evaluation points.

Tests¶

Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.

Open Part Exam

Back to Stage¶

Return to Stage 0: Orientation.