AI curriculum

Course outline for Web Discovery in the AI Era

A retrieved page can be easy to find yet fail to support an answer or contain outdated claims. Audit a sample website for clear facts, stable identifiers, source attribution, and freshness. Test fixed questions locally and improve evidence availability without promising rankings, AI citations, or answer-engine inclusion.

Technology: AI-Native Web and Application Development
Duration: 4 hours
Modules: 2 · 2 hours each
Level: Level 1

About Web Discovery in the AI Era

Web Discovery in the AI Era Course Objectives

Compare traditional indexing and AI retrieval needs using supplied examples.
Revise page structure to make entities and facts explicit.
Inspect structured markup against visible content rather than treating schema validity as truth.
Map answers to sources and page versions and flag stale or conflicting claims.
Compare fixed-question retrieval and answer support before and after revision.

Pre-requisites

Familiarity with website content or publishing.
Basic HTML awareness is helpful; coding is not required.
Ability to review a page and source record; knowledge-modeling experience is optional preparation.

Before this course

Complete the required courses or bring equivalent experience. Recommended courses add useful depth; they are not enrolment gates.

No prerequisite course is required. The entry skills above still apply.

Recommended preparation

Knowledge Engineering Foundations for AI Systems

Has publishing/website experience; basic HTML is helpful. Full web programming and agent engineering are not required for a content/retrievability audit.

Find your place in the learning map

Lab Setup

Computer with a browser and text editor; a spreadsheet is optional.
Use local sample pages, supplied structured-data examples, and a guided fixed-question retrieval worksheet or local search fixture.
No coding, paid SEO tool, analytics account, or external search-engine access is required.

Detailed Course Outline

Module 1: Discovery and clear content

Traditional SEO: Compare indexing-oriented page structure, clear entities, and stable identifiers using supplied examples; distinguish local improvements from external ranking claims.
AI search: Compare question-driven retrieval with conventional indexing; test whether relevant pages are found separately from whether their evidence supports an answer.
Answer engines: Inspect supplied answers for source support, freshness, and conflicts; distinguish plausible synthesis from justified answers without promising external inclusion.
Structured content: Revise entity names, factual sections, and stable identifiers; check structured facts against visible page content rather than trusting valid markup alone.

Practical outcome: Audit a local page and supplied search examples, then revise entity names, stable identifiers, factual structure, and markup/content alignment without predicting external visibility.

Module 2: Retrieval and source support

Machine-readable websites: Align supplied structured-data descriptions with visible facts and source references; identify representation gaps without treating machine readability as truth.
Retrieval by agents: Run fixed local questions before and after revision; distinguish retrieval success from supported answers and abstain when evidence is missing.
Content provenance: Map claims to sources, page versions, and freshness rules; flag stale or conflicting evidence and preserve references for supported answers.

Practical outcome: Produce a page evidence map with source/version/freshness rules; use fixed local questions to distinguish retrieval success from supported answers and abstain on absent or conflicting evidence.

Practical exercise

Submit a revised sample page, evidence map, and before/after local retrieval comparison.
Test at least six fixed questions covering supported facts, missing evidence, an outdated statement, and a source conflict.
Cite a source and page version for each supported answer; flag every seeded stale/conflicting case and abstain when evidence is absent.
Check that every structured fact matches visible page content and report retrieval changes separately from answer support, with no ranking or citation claims.

What you will produce

Audit and revise a page for source-supported local retrieval.

How the work is checked

At least six fixed questions separate retrieval success from source-supported answers.
All seeded missing, stale, and conflicting evidence receives the required flag or abstention.
Structured facts match visible content and supported answers include source/version references.

Four-hour noncoding page audit; live SEO, current search policies, and markup implementation require separately verified sources.

When another approach is better

Structured markup cannot compensate for absent, contradictory, or unsupported content.
A local retrieval comparison cannot predict rankings or inclusion in an external answer engine.
Do not add graph or search infrastructure when clearer page content solves the evidence problem.

How we train

Ask about exercises, instructor feedback and the prior knowledge you need. Tool-specific courses marked provisional may become modules in a broader course.

Discuss your learning goals