LLM Chunking, Indexing, Scoring and Agents, in a Nutshell. The new PageRank of RAG/LLM. With details on building relevancy scores.

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The post explains key concepts in AI and LLM terminology: chunking, indexing, agents, and relevancy scores. Chunking involves splitting a text corpus into small entities, indexing assigns identifiers for efficient retrieval, agents refer to the type of information retrieved by user prompts, and relevancy scores help rank the most relevant text entities in response to queries. The implementation examples are based on the author's experience with the xLLM system, tailored towards enhancing performance in enterprise contexts.