06. Module 14 Review — Diffusion Models

Companion files: Weekly Plan · Explainer · Study Material · Daily Recall · Assignment

Focus: forward and reverse diffusion, latent-space reasoning, conditioning, guidance, speed techniques, and production trade-offs.

Review loop

1. Re-answer every self-check question in 01_weekly_plan.md from memory.
2. Use 04_daily_recall.md and answer all 15 prompts aloud.
3. Run the four retrieval prompts in 02_explainer.md as a timed self-test.
4. Re-skim 03_study_material.md only after answering from memory.
5. Review 05_hands_on_lab.md and write down what became clearer after shipping.

Reflection

• What finally made the forward process and reverse process click?
• Which diffusion concept still feels hand-wavy under pressure?
• What would you struggle to explain on a whiteboard today?
• What must become automatic before Module 15?

Embedded checkpoint

This module closes the reasoning-and-multimodal phase. Use the checkpoint below as a seriousness test.

Conceptual — Reasoning

1. Reasoning model vs CoT prompting — what is the underlying difference?
2. When does a reasoning model not help?
3. ToT vs CoT — when is extra compute worth it?
4. RLHF: which step would you remove and why?
5. DPO removes what complexity?
6. PPO is unstable — what causes that instability?

Conceptual — Image / Video

1. GANs vs diffusion — why did diffusion mostly replace GANs?
2. CLIP's contrastive objective — what exactly is being optimized?
3. VLM architecture — how does vision connect to the language backbone?
4. Why is video generation harder than image generation?
5. CLIP for retrieval — what is the operational workflow?
6. ViT vs CNN — when would you still pick each?

Conceptual — Diffusion

1. Write the closed-form x_t expression and define every variable.
2. What does the denoiser predict and why is MSE enough?
3. Why does latent diffusion exist? What does the VAE contribute beyond compression?
4. Classifier-free guidance — training trick, inference formula, practical trade-off.
5. Name two speed techniques and state what each buys you.
6. DiT vs U-Net — architectural difference and scaling implication.

Applied

1. Design a text-to-image API serving 1000 req/min under a latency budget.
2. Design a multimodal product search system for e-commerce.
3. When would you choose a reasoning model vs a fast model in production?
4. How would you route between text-only and vision-capable paths cost-effectively?
5. If a diffusion system starts ignoring prompts, what knobs do you inspect first?

Foundation-gap check

Before Module 15, confirm all four are true:

• [ ] I can explain how diffusion generates images from noise.
• [ ] I can explain latent space simply and concretely.
• [ ] I can explain how conditioning enters the model.
• [ ] I can explain the speed-vs-quality trade-off in deployment.

If not, revisit the Foundation-Gap Audit in 02_explainer.md.

Self-evaluation

Section	Score	/
Reasoning conceptual	__	12
Image/Video conceptual	__	12
Diffusion conceptual	__	12
Applied	__	10
Total	__	46

End-of-phase reflection

1. What is your sharpest single sentence about your AI engineering identity now?
2. Which module in this phase had the highest signal-to-noise ratio?
3. Which hands_on_lab are you most proud of and why?
4. Where are you weakest right now?
5. What would you do differently in another 16-week pass?
6. Which Year-2 specialization feels most natural now?
7. What is your next 30-day plan?

Bridge to Module 15

Next module — 33_capstone_project — brings everything together. You will build a complete AI system using multiple techniques from all prior modules.

Use this review as your readiness gate. If the four foundation boxes above are not all true, pause and close those gaps first.

Completion gate

• [ ] Weekly plan completed
• [ ] Assignment shipped
• [ ] All four retrieval prompts answered from memory
• [ ] Foundation-gap check passed
• [ ] Score >= 35/46 on the checkpoint above
• [ ] Ready to move to Module 15