Concepts and mechanisms of the generative Web.
RAG, multimodal models, linked data and models running locally are distinct building blocks. Explaining them requires precise accounts of what works, under which conditions, and which limitations remain.
No live news collection or AI model connection is enabled in this preview.
Every dossier connects mechanisms, cases, limitations and original documents. Independent scientific review remains pending; original sources are provided for verification.
Identify errors at each stage of retrieval-augmented generation.
A RAG system selects candidate passages and supplies them to a model to draft an answer. Document identity, edition, date and rights should remain attached to indexed fragments; otherwise citations lose context.
A system can retrieve relevant passages yet write an unfaithful answer, or generate plausible prose from irrelevant evidence. Evaluate retrieval, reranking, faithfulness, citation accuracy and abstention when evidence is missing separately.
Understand relationships between semantics, metadata and AI systems.
JSON-LD represents resources and relations in JSON with a context defining identifiers. Linked data is neither an agent instruction nor a promise that a model will reproduce the content.
Page descriptions can clarify resource type, author, date or relationships. Google requires structured data to match visible content; artificial or misleading markup does not necessarily improve search eligibility.
Understand a Web API designed for inference on user devices.
WebNN aims to expose neural computation that can use processors and dedicated accelerators. Its purpose is not to provide a complete chatbot but a way to construct and execute suitable computational graphs.
Device and implementation affect supported operators, precision and execution time. Applications should inspect actual capabilities and benchmark representative models rather than rely on theoretical claims.
Separate GPU computation from model-level AI logic.
WebGPU lets Web applications render graphics and perform certain computations on GPUs. As of 13 November 2026 the consulted W3C publication is a Candidate Recommendation Draft; availability is not identical across all devices.
AI runtimes may use WebGPU for particular operations. WebGPU alone contains neither a model nor a dataset; it provides computation primitives.
Choose model architecture proportionate to tasks and devices.
Not every feature needs a large generative model. Conventional search, explicit rules and specialized models can solve many tasks with lower complexity and cost.
Compare quality, response time, energy, bandwidth, model size and actual costs. Parameter count alone does not summarize footprint or performance across devices.