search10 min read
AI-Powered Search — Building Semantic Search That Actually Works
Implement hybrid search combining keyword BM25 with semantic embeddings, ranking, and LLM-powered query understanding.
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Embeddings
13 articles
recommendations12 min read
AI Recommendation Systems — Embedding-Based Collaborative Filtering at Scale
Build recommendation systems using embeddings, two-tower models, and solve cold start with hybrid approaches.
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Dense-Passage-Retrieval8 min read
Dense Passage Retrieval in Production — Training and Deploying DPR Models
Build production DPR systems: train dual encoders, fine-tune on domain data, scale with FAISS, and outperform BM25 on specialized domains.
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embeddings9 min read
Fine-Tuning Embeddings for Your Domain — When Generic Models Are Not Enough
Fine-tune embeddings for specialized domains. Generate training pairs with LLMs, train with sentence-transformers, and deploy custom embedding models in production.
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embeddings8 min read
Embedding Model Comparison — OpenAI, Cohere, and Open-Source Options
Compare text-embedding-3-small vs 3-large, Cohere embed v3, sentence-transformers, multilingual models, and how to choose embeddings for your stack.
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Multimodal9 min read
Multimodal Embeddings — Searching Across Text, Images, and Audio Together
Master multimodal embeddings: CLIP for text-image, ImageBind for audio/3D, cross-modal search, and production storage strategies.
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postgres8 min read
pgvector in Production — Semantic Search and Similarity Queries at Scale
Master pgvector setup, index tuning, hybrid search patterns, and embedding pipelines for production semantic search at scale.
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postgresql7 min read
pgai — Running AI Directly Inside PostgreSQL
pgai extends PostgreSQL with AI capabilities: auto-embedding, semantic search, and LLM function calls—all in SQL. No external vector database required.
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RAG10 min read
RAG Chunking Strategies — How You Split Documents Changes Everything
Explore chunking strategies from fixed-size to semantic splitting, including sentence-window retrieval and late chunking techniques that dramatically improve retrieval quality.
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rag11 min read
RAG Chunking Strategies — Why Your Chunk Size Is Killing Retrieval Quality
Master semantic chunking, recursive splitting, parent-child strategies, and late chunking to maximize RAG retrieval quality and cut retrieval latency.
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RAG10 min read
Hybrid Retrieval for RAG — Combining Dense and Sparse Search
Explore why dense embeddings alone fail, and how hybrid search combining vector similarity with BM25 sparse retrieval dramatically improves RAG quality.
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RAG10 min read
Reranking for RAG — Why Your Top-K Retrieved Chunks Are Wrong
Understand why vector similarity ranks poorly, how cross-encoder rerankers fix it, and implement production-grade reranking with latency optimization.
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vector-databases6 min read
Vector Database Comparison 2026 — Pinecone, Weaviate, Qdrant, and pgvector
Compare the top vector databases in 2026: Pinecone serverless, Weaviate multi-tenancy, Qdrant quantization, pgvector for Postgres, and when to use each.
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