Self-check in your browser
Each unit carries its own checkpoints at the foot of its page, with diagnostic feedback. Your progress page collects all of them, and what you have recorded so far, in one place. Nothing to install, nothing uploaded.
DEVELOPMENT REVIEW DEPLOYMENT - NOT READY FOR RELEASE
Before you start
The program often called the first ever written computed Bernoulli numbers — a description Unit 1 shows is contested, which is a good place to start. Let's use it to learn how to work with — and check — the newest kind of machine. Ada wrote an exact, ordered procedure; a language model predicts plausible code. Ask the AI to reconstruct her computation, then do what her era couldn't do cheaply: verify it. The arc deterministic algorithm → statistical generation → human verification is the whole literacy story in one example.
Two things make this work rather than just read. First, every unit has a checkpoint you can pass or fail — a wrong answer tells you which misconception you have hit. Second, there is a verification lab that judges generated code against an exact reference.
Choose your route
Routes differ in where they stop, not in what they teach. Pick one and the previous/next links on every page follow it; you can change it here at any time.
Your work stays with you
The optional progress tools save only in your current browser profile. Browser data can be lost, so keep important answers separately and download regular backups.
How to check your work
Self-check in a terminal
Clone the repository and run the verification lab and the same checkpoints locally, using the Python standard library. Nothing to install beyond Python 3.
The units
0. Human introduction and how to use this module
How to use the module, what AI helped produce, and the learning contract you are agreeing to.
1. What generative AI is for
Five classes of task sorted by where the reference lives, and why that -- not the topic -- decides what checking costs.
2. Ada's ordered procedure
Lovelace, the Analytical Engine, Note G, and what it takes to specify a computation exactly.
3. What a language model does differently
Next-token prediction set against deterministic execution, and why plausibility is not correctness.
4. Choosing the tool
Four kinds of system, what each one changes about the checking you owe, and what a query costs in money, time and energy.
5. Prompts, context and attachments
What actually reaches the system, why the window has an edge you cannot see, and why an attachment makes an answer checkable rather than true.
6. Meet the Bernoulli numbers
Enough mathematics to know what correct output looks like — including the two convention traps that make correct programs disagree.
7. Ask AI to generate the routine
A deliberately underspecified prompt, and the plausible mistakes it produces.
8. Verification lab
Run generated code against an exact oracle, find the first divergence, and name the failure mode.
9. Grounding and citation checking
The module's second worked case, run on a question with no oracle -- what a citation establishes, and six named ways one fails.
10. Re-prompting, specification, and overreliance
Turning a vague request into testable requirements. Every clause is a bug that already happened.
11. Agents and automated actions
What changes when a system acts rather than answers -- an action can change the world before you have looked at it, so sort by what recovery costs and check the calls rather than the report.
12. High-stakes use and escalation
A second axis -- how much checking, from consequence, reversibility and your own uncertainty -- and the row where the answer is another person.
13. Privacy and data handling
What actually leaves your device, what is retained, and a classification by whose material it is rather than by how secret it feels.
14. Copyright and attribution
Three obligations owed separately -- rights, attribution and disclosure -- and honesty about which of them has unsettled answers.
15. Bias, representation and accessibility
The method run on its hardest object -- what one controlled comparison establishes, why absence escapes it, and the part with definite answers.
16. The historical bug and historical honesty
An error in a respected published table, the difference between an established fact and a repeated claim, and the module's own mistake with both.
17. Disclosure, defence, and responsible use
State what AI did, what you checked, and why your answer is right — three different statements.
18. Alles Lüge! First- and second-order reasoning
The closing unit. Where the observer stands when it checks something, why that question is older than AI, and how to sort claims by the evidence actually behind them.
Reference material
- PDF handbook — the complete course material in a single printable document, without the answers, so you can still attempt the work from it.
- PDF handbook, guidance edition — the same document with the marking guidance and the checkpoint answers. It is the guidance this site shows you after you answer; open it when you have attempted the work.
- Glossary — terms used across the module.
- References and source policy — what is cited, and the rules for citing it.
- Historical notes — the authoritative reference for every historical claim, with each marked established or contested.
- Evidence pack template — what you should have produced by the end.
- Second-order reasoning — the general argument the module is a worked instance of. Background for Unit 9.
- Misconceptions — expected errors across the module, and where each is addressed.
- Authorship and credits — who made what, including which parts were AI-generated.