Context Engineering
● Retrieval & Context Context Engineering Context Engineering focuses on providing the right knowledge, structure and context at the right time. High-quality context often has a greater impact on AI…
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
● Retrieval & Context Vector Search Vector Search uses embeddings to identify content that is contextually related to a query. This enables more accurate and meaning-based information retrieval. MechanicsHow It…
Grounding
● Retrieval & Context Grounding Grounding is the process of connecting AI-generated outputs to reliable and verifiable information sources. It helps ensure that responses are based on facts rather than…
Content Chunking
● Retrieval & Context 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. MechanicsHow…
RAG (Retrieval-Augmented Generation)
● Retrieval & Context 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…
LLMs.txt
● Retrieval & Context 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…