#RAG(9)

July 2026
#AI #LLM #RAG #VectorSearch #Python

How HNSW Vector Search Actually Works

Every RAG stack leans on HNSW but treats it as a black box. Here is how the layered graph index finds nearest neighbors fast, and the knobs that matter.

Read more →
July 2026
#AI #LLM #RAG #Python #FastAPI

Cross-Encoder Reranking for RAG Pipelines

Retrieval puts the right chunk at rank 8, but the generator only reads the top few. How a cross-encoder reranker reorders RAG candidates, and where it fails.

Read more →
July 2026
#AI #LLM #RAG #Python #FastAPI

Chunking Strategies for RAG Pipelines

How to chunk documents for a RAG pipeline: why fixed-size splitting fails, structure-aware splitting, size and overlap tradeoffs, and the failure modes.

Read more →
July 2026
#AI #LLM #RAG #Evaluation #Python

Measuring RAG Retrieval Quality: recall@k, MRR

Changed your embeddings or added a reranker? Measure it. Build a golden set and score retrieval with recall@k, MRR, and nDCG before you trust the change.

Read more →
July 2026
#AI #LLM #RAG #OpenSearch #Python

Hybrid Search for RAG: BM25 + Vectors

Vector search alone misses exact IDs and error codes. Here's how to combine BM25 keyword search with dense retrieval, fuse the rankings with RRF, and rerank.

Read more →
June 2026
#AI #LLM #RAG #FastAPI #Python

Incremental Indexing for RAG Pipelines

How to keep a RAG index fresh without full rebuilds: detect changed files with checksums, re-embed only what changed, and handle deletions safely.

Read more →