Pragmatic AI Labs

Grounded AI: Knowledge Graphs and Validation for Agents

Pragmatic AI Labs

Grounded AI: Knowledge Graphs and Validation for Agents

Noah Gift

Instructor: Noah Gift

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Tell terminology from assertions, and write the axioms a corpus needs before it can even be wrong

  • Read and write SHACL shapes, and read the report a violation produces: node, path, value

  • Place a constraint outside the model, on the output side, where it can refuse

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Recently updated!

September 2026

Assessments

5 assignments

Taught in English

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There are 4 modules in this course

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.

What's included

5 videos5 readings1 assignment

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.

What's included

4 videos4 readings1 assignment

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.

What's included

3 videos4 readings1 assignment

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.

What's included

2 videos4 readings2 assignments

Instructor

Noah Gift
Pragmatic AI Labs
61 Courses11,689 learners

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