01Extended dossier
What is WebAI?
The AI-generated Web is more than model-written text.
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WebAI encyclopedia◇
36 reference dossiers connecting technology, standards, primary publications, use cases and limitations.
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36 source-linked reference dossiers with mechanisms, use cases, limitations and primary texts. Independent scientific and editorial review remains pending.
02Extended dossier
Understanding the Generative Web
Creation, adaptation and publication with generative models.
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RAG: retrieval-augmented generation
Retrieve documents to inform a generated answer.
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The Agentic Web
Agents, tools, permissions and control of actions.
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Understanding MCP
Connect AI applications to external tools and resources.
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Understanding A2A
Enable independent agents to collaborate.
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WebMCP: an interface for Web agents
Expose structured capabilities from a Web application.
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C2PA and content provenance
Describe the origin and some edits of a media asset.
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WebGPU and in-browser AI
Use the GPU to accelerate computation and interfaces.
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From Transformers to multimodal models
Understand model families and their limitations.
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SEO, AEO and GEO: key differences
Visibility in conventional search and answer interfaces.
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Knowledge graphs and JSON-LD
Structure links between concepts, sources and technologies.
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Accessibility and AI: design for everyone
Interfaces usable by keyboard, readable and understandable.
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WebXR and generative experiences
Potential intersection of immersive Web and generative AI.
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Evaluating agents and their limits
Tests, partial success, safety and reproducibility.
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Prompt injection and agent security
Keep authorized instructions separate from untrusted data.
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Visibility in generative search
Discover and evaluate content inclusion in AI answer interfaces.
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Control search and AI crawlers
Understand the distinct roles of indexing, search and training crawlers.
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llms.txt: proposal and limits
Avoid treating an optional convention as a universal ranking standard.
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Build websites for humans and agents
Make interfaces easier to interpret without degrading human experience.
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MCP: versions, capabilities and security
Understand protocol evolution without conflating adoption with universal maturity.
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A2A: discovering and describing agents
Understand agent cards without oversharing internal information.
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WebMCP: JavaScript tools exposed by a page
Study the experimental API and the exact status of the community draft.
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Human oversight of Web agents
Design approval gates before consequential actions.
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Indirect prompt injection through Web content
Separate authorized instructions from untrusted retrieved Web content.
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Assess risks of the generative Web
Structure risk assessment rather than relying on promises.
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Built-in browser AI: capabilities and limits
Assess translation, summarization and generation APIs offered by some browsers.
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WebNN and neural acceleration
Understand a Web API designed for inference on user devices.
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WebGPU for computation and Web models
Separate GPU computation from model-level AI logic.
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Rigorous evaluation of RAG systems
Identify errors at each stage of retrieval-augmented generation.
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Structured data, JSON-LD and discoverability
Understand relationships between semantics, metadata and AI systems.
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C2PA provenance and independent verification
Avoid conflating signatures, attribution and factual truth.
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EU transparency for AI-generated content
Read transparency requirements without generalizing beyond applicable law.
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Accessible and understandable AI interfaces
Apply WCAG to dynamic content and AI interactions.
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Right-sized AI and sustainability on the Web
Choose model architecture proportionate to tasks and devices.
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Teaching critical AI literacy for the synthetic Web
Move from passive acceptance of answers to evidence-led inquiry.
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