17 terms
Definitions in this topic
- Agentic AIAgentic RAGAgentic RAG explained: how it differs from standard RAG, the retrieve-evaluate-retry loop, and when to use it.
- Agentic AIAgentic SearchAgentic search explained: the multi-step retrieval loop that plans queries, checks evidence, and retries before answering.
- LLM foundationsContextual RetrievalContextual retrieval explained: how document-aware chunk prefixes improve keyword and vector search in a RAG system.
- LLM foundationsDocument chunkingDocument chunking explained: splitting source material into retrievable passages that preserve enough context for RAG.
- LLM foundationsEmbeddingsEmbeddings explained: how a text-to-vector model captures meaning, and why the choice of model matters for retrieval.
- LLM foundationsGraph RAGGraph RAG explained: using entities, relationships, and graph summaries to ground answers across connected data.
- LLM foundationsHybrid searchHybrid search explained: combining keyword and vector retrieval to improve RAG relevance across exact and semantic queries.
- LLM foundationsKnowledge graphKnowledge graphs explained: representing entities and relationships as structured, queryable context for AI systems.
- LLM foundationsLate ChunkingLate chunking explained: how document-level context improves RAG embeddings for chunks that depend on surrounding text.
- Agentic AILlamaIndexLlamaIndex explained: ingestion, indexing, and query engines over your own data, and how it differs from LangChain.
- LLM foundationsMetadata filteringMetadata filtering explained: combining structured constraints with vector search for precise and permission-aware retrieval.
- LLM foundationsQuery RewritingQuery rewriting explained: how RAG systems reformulate questions to retrieve better evidence without changing the user's intent.
- LLM foundationsRAGRAG explained: the query, retrieve, augment, generate loop, and where it breaks in a real production system.
- LLM foundationsRerankingReranking explained: rescoring top retrieval candidates with a stronger model before they enter an LLM context.
- LLM foundationsSemantic searchSemantic search explained: retrieving by meaning with embeddings rather than relying only on exact keyword matches.
- LLM foundationsSparse RetrievalSparse retrieval explained: keyword-based search, BM25, and why exact terms still matter in a modern RAG pipeline.
- LLM foundationsVector databaseVector database explained: how similarity search works, and what actually separates one option from another.