Context Engineering
Context Engineering How It Works Context Engineering is the practice of designing and managing the information environment that AI systems use to understand and perform tasks.Rather than focusing only on…
In our Knowledge Hub – Retrieval section you learn the key terms, concepts and most relevant topics behind Semantic Brand Architecture. This section breaks down common terminology into straightforward insights you can quickly understand and apply.
Vector Search
Vector Search How It Works Vector Search retrieves information based on semantic similarity rather than exact keyword matches.Content is represented as embeddings within a multidimensional vector space. Strategic Importance Vector…
Grounding
Grounding How It Works Grounding refers to anchoring AI-generated outputs in verifiable information sources.A grounded system connects responses to reliable data, documents, databases or knowledge repositories. Strategic Importance Grounding improves…
Content Chunking
Content Chunking Content Chunking refers to the division of content into smaller, semantically consistent information units. This improves AI processing, retrieval precision and citation accuracy. How It Works Content Chunking…
RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) RAG describes an AI architecture in which a language model retrieves external knowledge sources before generating a response. This allows answers to be based not only on…
LLMs.txt
LLMs.txt An llms.txt file is a structured website file that provides guidance to Large Language Models regarding which content is relevant, quotable or preferred for AI interpretation. Like a robots.txt…