Tag: #embedding

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• The Technique of Hallucinating Instead of Classifying, and How to Validate It — Why a Fake Label Beats the Raw Query

  Instead of putting a taxonomy of hundreds of entries into a prompt, have a small model invent a plausible fake classification and map it onto the real taxonomy with embeddings. Why that can work is explained by asymmetry

  2026-08-14 · 7 min read #llm#embedding#classification#search#retrieval

• Embeddings and Rerankers: What Actually Matters in RAG

  What decides your embedding model in a RAG build is not a leaderboard rank but dimensionality, maximum input length, prefix conventions, multilingual coverage, and the division of labor with a reranker. This post lays ou

  2026-08-12 · 7 min read #ai#llm#huggingface#open-source-llm#embedding

• SOTA Text Embedding Models — The Heart of Search and RAG

  Text embeddings are the heart of search and RAG systems. We walk through contrastive learning and InfoNCE, dual encoders, hard negatives, the E5/BGE/GTE families, Matryoshka representation learning, and the MTEB benchmar

  2026-06-30 · 14 min read #ai-papers#embedding#retrieval#rag#contrastive-learning

• ANN Algorithms Complete Guide 2025: HNSW, IVF, Product Quantization, LSH — How Vector DBs Really Work

  How Pinecone, Weaviate, Qdrant, and pgvector find similar items in milliseconds across hundreds of millions of vectors. A deep dive into the math and implementation of HNSW, IVF, Product Quantization, and LSH.

  2026-04-15 · 14 min read #ann#hnsw#ivf#product-quantization#lsh

• Vector Database Complete Guide 2025: Embeddings, Similarity Search, Pinecone/Weaviate/Qdrant/pgvector ♪ Listenable

  Everything about Vector DBs! Vector embedding fundamentals, similarity search (cosine/euclidean/dot product), indexing algorithms (HNSW/IVF/PQ), Pinecone vs Weaviate vs Qdrant vs Milvus vs pgvector comparison, hybrid sea

  2026-04-13 · 23 min read #vector-database#embedding#similarity-search#pinecone#weaviate

• Advanced RAG Pipeline Complete Guide 2025: Chunking Strategies, Re-ranking, Agentic RAG, Evaluation ♪ Listenable

  Everything about Advanced RAG! Document chunking strategies (Semantic/Recursive/Agentic), re-ranking (Cohere/ColBERT/Cross-encoder), Query Transformation, Agentic RAG (Self-RAG/CRAG), Multi-modal RAG, Evaluation (RAGAS/T

  2026-04-13 · 23 min read #rag#chunking#reranking#agentic-rag#evaluation

• Embedding Model Selection Guide 2025: From OpenAI to Open-Source Options

  A practical 2025 comparison of the major embedding models — OpenAI, Cohere, BGE-M3, E5-mistral, nomic-embed — with MTEB benchmark analysis, language-specific recommendations, and a decision framework for choosing the rig

  2026-03-18 · 9 min read #embedding#embedding-model#rag#ai-development#openai

• RAG Systems Complete Guide: Everything About Retrieval-Augmented Generation ♪ Listenable

  A complete guide to mastering RAG (Retrieval-Augmented Generation) systems. From basic RAG to advanced Self-RAG, Corrective-RAG, and HyDE — covering vector databases, embedding models, chunking strategies, and reranking

  2026-03-17 · 23 min read #rag#llm#vector-database#langchain#embedding

• LLM, Tool Calling & Embedding Benchmarks Deep Dive: What Each Benchmark Actually Measures ♪ Listenable

  Complete analysis of major AI benchmarks — MMLU, HellaSwag, HumanEval, BFCL, MTEB, RAGAS and more. Understand exactly what each benchmark measures, score interpretation, limitations, and which benchmarks to use for your

  2026-03-17 · 24 min read #llm#benchmarks#mmlu#mteb#bfcl

• Complete Guide to Embedding Models: Vector Search, RAG, and Sentence Transformers in Practice ♪ Listenable

  A comprehensive guide covering embedding fundamentals, key model comparisons (OpenAI, Cohere, BGE, E5, GTE, Jina), Sentence Transformers usage, vector databases (Pinecone, Weaviate, Milvus, Chroma, FAISS) and indexing st

  2026-03-13 · 22 min read #llm#embedding#vector-search#rag#sentence-transformers

• RAG Pipeline Production Guide: From Vector DB Selection to Chunking, Reranking, and Evaluation

  A comprehensive guide to building RAG (Retrieval-Augmented Generation) pipelines for production. Covers embedding model comparison, vector DB selection (Pinecone, Milvus, Weaviate, Qdrant, Chroma), chunking strategies, h

  2026-03-11 · 17 min read #llm#rag#vector-database#retrieval#embedding

• Complete Guide to Qdrant Vector DB Operations — From Collection Design to RAG Integration

  A practical guide covering Qdrant vector DB core concepts, collection design, index strategies, payload filtering, RAG pipeline integration, and production monitoring.

  2026-03-09 · 11 min read #database#qdrant#vector-database#embedding#similarity-search

• PostgreSQL + pgvector Vector Search Practical Guide: From RAG to Hybrid Search

  Implement vector similarity search with the PostgreSQL pgvector extension. Covers installation through HNSW/IVFFlat indexes, hybrid search (vector + full-text search), and RAG pipeline integration with practical code.

  2026-03-02 · 9 min read #postgresql#pgvector#vector-search#embedding#rag