Week 1 Optional Weekend Assignment
This weekend, choose Quick Review or Extended Review if you want additional practice. You may also choose no homework without penalty.
Choose Your Weekend Route
Quick Review · 8-Minute Video + One Question
3Blue1Brown: Large Language Models explained briefly
https://www.youtube.com/watch?v=LPZh9BOjkQs
Optional assignment task: watch the video, then complete the Quick Review Response below by writing one question you still have.
Extended Review · 83 Minutes of Video + Writing
Andrej Karpathy: Deep Dive into LLMs like ChatGPT
https://www.youtube.com/watch?v=7xTGNNLPyMI
Optional assignment task: watch 00:01–00:31, 00:59–01:42, and 03:21–03:31, then answer the six Extended Review prompts below. The 83-minute label covers video only; allow additional time for writing.
No Homework · No Penalty
You may stop after the Week 1 class activities. Bring your ungraded Week 1 evidence card back only if it helps your recap.
Choosing this route does not affect your course grade or participation in Week 2.
Deep Dive · Optional Enrichment
Watch the complete 3-hour-31-minute Karpathy lecture and use all 18 timestamped checkpoints.
This route is beyond the optional weekend assignment. The time label covers video only; allow additional time for checkpoints. You may split the work across sittings or change playback speed.
https://karpathy.github.io/2026/02/12/microgpt/
Create Your eCampus Submission
Enter your response here, create a concise PDF, and upload that PDF to the Week 1 assignment in eCampus.
Quick Review Response
Extended Review Responses
Open the Karpathy video ↗ Watch 00:01–00:31, 00:59–01:42, and 03:21–03:31.
Answer in your own words. Short, specific responses are better than copied definitions.
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- Open the Week 1 assignment in eCampus, upload the saved PDF, and select Submit.
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Optional Baseline
In one sentence, what do you think ChatGPT is doing when it answers a question?
Tiny Glossary
| Term | Meaning for this course |
|---|---|
| Token | A chunk of text the model processes. It can be a word, part of a word, punctuation, or code fragment. |
| Context window | The current working memory: the text the model can see while answering. |
| Pretraining | Learning broad patterns from massive text and code data. |
| SFT | Supervised fine-tuning: training from example conversations or desired outputs. |
| RLHF | Reinforcement learning from human feedback: using human preferences to shape model behavior. |
| Inference | Running the trained model to generate output, usually one token at a time. |
| Hallucination | A fluent answer that is unsupported, misleading, or false. |
| Tool | An external action the system lets the model request, such as search, calculation, or code execution. |
| Guardrail | A rule, approval step, or check that limits risky behavior. |
Deep-Dive Route: 18 Optional Checkpoints
Timestamps are based on the current YouTube chapters for "Deep Dive into LLMs like ChatGPT." Use them as anchors, not as exact quiz boundaries.
00:01:00Pretraining data (internet)
What kinds of data help train an LLM? Why does data quality matter?
00:07:47Tokenization
What is a token? Give one example where tokenization might surprise a user.
00:14:27Neural network input/output
What goes into the model, and what comes out?
00:20:11Neural network internals
You do not need the math. What is the useful mental model for what the network has learned?
00:26:01Inference
Why does the answer appear one piece at a time?
00:31:09GPT-2: training and inference
What does a base model do? How is that different from a helpful chat assistant?
00:42:52Llama 3.1 base model inference
What did you notice about interacting with a base model?
00:59:23Pretraining to post-training
Why is pretraining not enough if we want a useful assistant?
01:01:06Post-training data (conversations)
How do conversation examples change the behavior of a model?
01:20:32Hallucinations, tool use, knowledge, working memory
Why can a model sound confident and still be wrong? How can tools help?
01:41:46Knowledge of self
Why might a model be confused about what it is, what version it is, or what it can access?
01:46:56Models need tokens to think
Why can writing intermediate reasoning or scratch work help a model perform better?
02:01:11Tokenization revisited
Why can spelling, counting letters, or manipulating strings be surprisingly hard?
02:04:53Jagged intelligence
What does it mean for a model to be brilliant at one task and weak at another?
02:07:28SFT to reinforcement learning
What changes when the system moves beyond examples and starts using feedback or rewards?
02:14:42Reinforcement learning
Explain reinforcement learning in one sentence using your own words.
02:48:26RLHF
Why do human preferences matter when building assistant-like models?
03:21:46Grand summary
Write three takeaways you want to remember before learning agents.
Bridge to Agents
Use this course definition:
Classify each example as chatbot answer, tool call, or agent loop.
| Example | Your classification | Why? |
|---|---|---|
| ChatGPT explains the difference between pretraining and post-training. | ||
| A model asks a calculator tool to compute 438 * 27. | ||
| A coding system reads files, proposes a plan, edits code, runs tests, and asks before deploying. |
Verification Habit
Pick one important claim from the video or from a chatbot answer. How would you verify it?
Exit Ticket
In 4-6 sentences, explain why an agent needs guardrails.
Optional Self-Check
Save Your Full Worksheet
This creates a PDF of every answer area on this page, including Optional Baseline, all 18 Deep-Dive checkpoints, Bridge to Agents, Verification Habit, Exit Ticket, and Self-Check. Unanswered areas are printed as (not answered) so nothing you typed is left out. Keep this for your own records; the eCampus submission still uses the Quick or Extended PDF above.
Every answer area on this page saves automatically on this device.