Learning objectives
By the end you can:
- State the seven durable tool categories (chat assistant, coding copilot, research/agentic, data-analysis, document/PDF, spreadsheet, presentation/design) and place any described AI product into the correct one, arguing why, even when its marketing blurs the line.
- Run the five-question tool-evaluation rubric, what is it? what's it good and bad at? where does your data go? how do you verify its output? what's its cost/offline profile?, on an unfamiliar tool using only its own documentation or website, inside a couple of minutes.
- For each of the seven categories, name its single most common failure mode and the Guardrail check (Trace · Match · Recompute · Date-check · Cite) that catches it.
- Distinguish durable information about a tool (its category, its mechanism, its typical failure shape) from perishable information (its name, price, version, or exact feature list), and explain why that split protects your judgment from marketing churn and dated tutorials.
- Read a tool's own data-usage or privacy documentation and correctly answer whether a given input (a paste, an upload, a query) is used to train future models, retained, or processed and discarded.
- Given a real analyst task, choose the right tool category (not brand) for it and justify the choice against at least one plausible alternative category.
- Explain why "free" and "offline" are two independent facts about a tool, not two words for the same thing, and why both matter when the input is sensitive company data.
- Apply the rubric, as a standing habit, to a tool that did not exist when this module was written, this is the productivity skill the module exists to install, not a one-time exercise.
- State this module's dated catalog is a snapshot as of a specific date, correctly locate a live source to verify any name or price in it, and describe the standing refresh ritual for keeping a personal tool catalog current.
The duality, stated once (R10). Objectives 2, 3, 5, and 7 are the understanding objective (the rubric skill, run by hand against a tool's own documentation) and this is what the mastery gate rewards; no subscription and no tool ever buys a pass on it. Objectives 1, 6, 8, and 9 are the productivity objective, the category map, the habit of re-running the rubric on anything new, and the dated snapshot of current examples you keep for day-to-day use. Awareness of what exists makes you fast; the rubric, applied by hand, makes you an analyst who cannot be fooled by a landing page.
Prerequisites & connections
Builds on. AI0.01's mechanism is the reason this catalog holds together at all: every category below (however differently it is branded, priced, or marketed) is built on the same next-token-prediction engine that optimizes for plausible, not true. That is why a chat assistant, a coding copilot, and a presentation-design tool share overlapping failure shapes (a confident wrong answer, an invented citation, a narrative that fits your data without being true to it) even though they look nothing alike on screen. If AI0.01 is not fresh, its one-sentence payoff is worth re-reading before you start: the model optimizes for plausible, not true. Nothing below requires code, a paid subscription, or prior tool experience, only that sentence, held firmly.
Feeds forward. AI0.03 (prompt engineering) assumes you can already place a tool in one of these seven categories and picks up from there, teaching you what to actually type into it. AI0.04 (retrieval and RAG) is the mechanism running underneath most of the document/PDF category and a growing share of the research/agentic one, you will recognize "chat with this filing" as a specific, grounded instance of the document/PDF category you meet here. AI0.05 (agents) reuses these same seven categories directly when it asks you to name, step by step, which category of tool a workflow needs at each stage, categories, never vendors, so a workflow design survives tool churn. AI0.06, the branch's crown, needs you to already know where a given tool's data goes and what its typical failure shape is, so that detecting a planted error is about the failure mode, not a first encounter with the tool. AI0.07's capstone applies the rubric across your whole analyst workflow (research, modeling, memo, deck, code, data) and adds the ethics, disclosure, and data-privacy layer this module only opens the door to. Like every node in this branch, AI0.02 gates nothing in the eleven-phase Finance sequence, and nothing outside AI0.01 gates entry here.