AI Knowledge Retrieval and Delivery
- [Dartmouth College]
- Overview
AI knowledge retrieval and delivery relies on core mechanisms like vector databases, semantic search, and retrieval-augmented generation (RAG) to surface precise facts. It transforms raw data into grounded, conversational answers.
1. Core Retrieval Processes:
- Intent Analysis: Parses natural language to extract core meanings, entities, and context.
- Vector Indexing: Converts text or media into numerical vectors stored in specialized databases for quick semantic matching.
- Hybrid Search: Combines keyword precision (like BM25) with semantic vector search for balanced accuracy.
2. Delivery and Generation:
- Context Augmentation: Wraps the retrieved document fragments around the user's initial prompt.
- Grounded Synthesis: Passes the enriched context to a large language model to draft a factual, source-cited response, preventing hallucinations.
- Agentic Execution: Empowers autonomous agents to plan, query multi-step data sources, and trigger workflows dynamically.
[More to come ...]
