AISuffer

RAG & Retrieval

RAG is how you give a model knowledge it was never trained on. These guides cover embeddings, chunking strategy, vector stores, reranking, and the failure modes that quietly wreck retrieval quality.

  1. 01

    Best Vector Database for RAG in 2026

    Guide intermediate

    pgvector vs Pinecone, Qdrant, Weaviate, and Milvus for RAG in 2026, with a benchmark-backed decision table and honest cost and scale tradeoffs.

  2. 02

    Embeddings Explained for RAG

    Concept beginner

    What embeddings are, how they power RAG, how similarity works, and how to pick a 2026 embedding model by MTEB score, dimensions, and price.

  3. 03

    Chunking Strategies for RAG

    Guide intermediate

    How to chunk documents for RAG in 2026: fixed-size, recursive, and semantic strategies, chunk size and overlap, plus the failure modes that wreck retrieval.

  4. 04

    How to Evaluate a RAG System

    Guide intermediate

    Measure RAG quality with retrieval and generation metrics, a golden set, and the right eval tools so you can find which stage fails.