Reranking

Summary

Using a second, more precise model to re-score and reorder an initial set of retrieved candidates — improves final result quality beyond what fast approximate retrieval alone provides.

How to Learn This

1Add a reranking step (e.g. a cross-encoder model) after initial vector retrieval.
2Compare top-result quality with and without reranking.
3Learn the latency/quality trade-off rerankers introduce.

Hybrid Search (Keyword + Vector)

Context Window Management

More topics in Retrieval-Augmented Generation & Vector Databases

Embeddings & Semantic Search Vector Databases (Pinecone, Chroma, Weaviate, FAISS)Chunking Strategies RAG Architecture Hybrid Search (Keyword + Vector)Context Window Management Evaluating RAG Pipelines Knowledge Graphs (Overview)

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