BLUF:RAG is an AI architecture that optimizes LLM output by referencing an authoritative, external knowledge base outside of its initial training data before generating a response.
BLUF:GEO is the process of optimizing website content to be accurately indexed, cited, and prioritized by AI-driven search engines like Perplexity, Gemini, and SearchGPT.
BLUF:An agentic workforce is a team of autonomous AI agents capable of planning, executing multi-step tasks, and using tools to achieve complex business objectives with minimal human intervention.
BLUF:A vector database is a specialized storage system that indexes and searches data as high-dimensional vectors, enabling fast similarity searches across unstructured data like text, images, and audio.
BLUF:Semantic search is a data retrieval technique that focuses on the intent and contextual meaning of a search query rather than just matching keywords.
BLUF:An LLM is a type of AI trained on vast amounts of text data, capable of understanding, generating, and manipulating human language with high levels of sophistication.
BLUF:Fine-tuning is the process of taking a pre-trained AI model and further training it on a smaller, specific dataset to adapt it for a particular task or industry.
BLUF:Prompt engineering is the strategic craft of designing and refining inputs (prompts) to guide LLMs toward generating the most accurate, relevant, and useful outputs.
BLUF:Multimodal AI refers to models capable of processing and generating information across multiple formats simultaneously, including text, images, video, and audio.
BLUF:LLM orchestration is the management of multiple AI models, tools, and data sources working in a coordinated sequence to complete complex, high-level business workflows.
BLUF:A context window is the maximum amount of information (tokens) an AI model can 'remember' and process at a single time during a conversation or task.
"Digital entities with clean, authoritative definitions are 85% more likely to be integrated into AI Knowledge Graphs."
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