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Grounded AI: Knowledge Graphs and Validation for Agents

Pragmatic AI Labs via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 2 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

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Four claims nobody checked, and one move that refuses them: write the terminology down, compile it into constraints, bind the claim to the thing it is about, and let a checker say no. You will separate terminology from assertions, read a SHACL shape line by line and the report it produces, watch a validator answer three ways rather than two, compute coverage as a set difference, and handle type hierarchies by deriving the closure offline so the checker on the path stays a string match. The course ends where it started: an agent's write path, and the one position from which something can refuse a change before it lands. None of this makes a model correct. It makes wrongness land somewhere visible, with a name, before it reaches anyone who would have believed it.

Syllabus

Module 1: Claims nobody checked
- Four claims nobody checked, and the one move that refuses them. A README, a config, a dashboard and a model answer all look like facts because nothing with the authority to refuse them ever held them; four honest counts of one repository never agree because no one wrote down what a test is. The move is to write the terminology down, compile it into constraints, bind the claim to the thing, and let a checker say no.
Module 2: Shapes and absence
- A shape file, line by line, and the report it produces. One shape over one ordinary artifact, verdict by verdict; a closed shape that refuses what it was not told about; and the same graph handed to two validators that answer opposite ways, both correctly, because silence means different things under the open and closed worlds.
Module 3: Binding a claim to a thing
- Nine things, four shapes, zero violations, and four things never examined. A shape declares what it is about, and coverage is a set difference nothing computes for you. A reasoner adds a fact nobody wrote, the shape runs against the graph that came out, and the parent-type rule that a string matcher never saw is derived once, offline, so the checker stays small.
Module 4: The gate
- Why grounding happens after the model, not inside it. Everything in the loop is input, and nothing in the loop can say the output is correct; two checks read the output, reviews initialise at fail, and a refusal is a location with a name. One move, four costumes, and the small promise it keeps: this does not make a model correct, it makes wrongness land somewhere visible before anyone believes it.