# Agentic Ai Engineering
All Agentic Ai Engineering notes →1. Retrieval-Augmented Generation (RAG)
Covers the core RAG pipeline of indexing, retrieval, and generation, and why retrieval quality bounds answer quality regardless of how large the LLM's context window is.
2. Embeddings
Explains how embedding models turn text into dense vectors and the dimensionality, cost, and quality tradeoffs an architect weighs when picking a commercial versus open-source embedding model.
3. Chunking Strategies
Compares fixed-size, recursive, and semantic chunking strategies and how chunk boundary choices determine whether retrieved passages preserve the meaning needed to answer a query.
4. Vector Search
Covers the approximate nearest neighbor algorithms (HNSW, IVF) behind vector databases and the recall, latency, and memory tradeoffs involved in choosing an index type at scale.
5. Hybrid Search
Explains why combining dense vector similarity with sparse keyword search (BM25) outperforms either alone, and how to fuse and weight the two result sets.
6. Reranking
Covers cross-encoder reranking models that reorder an initial retrieval candidate set for precision, and when the added latency cost is justified in a production pipeline.
8. Agentic RAG
Covers RAG architectures where an agent decides when and what to retrieve, iteratively refining queries and evaluating retrieved evidence rather than retrieving once up front.
9. GraphRAG
Explains how knowledge-graph-structured retrieval captures entity relationships and multi-hop reasoning that pure vector similarity search misses.
10. Multi-Stage Retrieval
Covers pipelines that chain coarse-to-fine retrieval stages, from candidate generation through filtering to reranking, to balance recall and precision at scale.