Compare LangChain, LlamaIndex, Semantic Kernel, and more. Learn when to use each framework and build production-ready RAG systems with practical code examples.
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RAG Query Optimization: Transforming User Questions into Effective Retrieval
Introduction: RAG quality depends heavily on retrieval quality, and retrieval quality depends on query quality. Users often ask vague questions, use different terminology than your documents, or need information that spans multiple topics. Query optimization bridges this gap—transforming user queries into forms that retrieve the most relevant documents. This guide covers practical query optimization techniques: […]
Read more →Advanced RAG Patterns: Query Rewriting and Self-Reflective Retrieval (Part 2 of 2)
Introduction: Basic RAG retrieves documents and stuffs them into context. Advanced RAG transforms retrieval into a sophisticated pipeline that dramatically improves answer quality. This guide covers the techniques that separate production RAG systems from prototypes: query rewriting to improve retrieval, hybrid search combining dense and sparse methods, cross-encoder reranking for precision, contextual compression to fit […]
Read more →Retrieval Evaluation Metrics: Measuring What Matters in Search and RAG Systems
Introduction: Retrieval evaluation is the foundation of building effective RAG systems and search applications. Without proper metrics, you’re flying blind—unable to tell if your retrieval improvements actually help or hurt end-user experience. This guide covers the essential metrics for evaluating retrieval systems: precision and recall at various cutoffs, Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative […]
Read more →Document Chunking Strategies: Optimizing RAG Retrieval Quality
Introduction: RAG systems live or die by their chunking strategy. Chunk too large and you waste context window space with irrelevant content. Chunk too small and you lose semantic coherence, making it hard for the LLM to understand context. The right chunking strategy depends on your document types, query patterns, and retrieval approach. This guide […]
Read more →Embedding Model Selection: Choosing the Right Model for Your RAG System
Introduction: Choosing the right embedding model is critical for RAG systems, semantic search, and similarity applications. The wrong choice leads to poor retrieval quality, high costs, or unacceptable latency. OpenAI’s text-embedding-3-small is cheap and fast but may miss nuanced similarities. Cohere’s embed-v3 excels at multilingual content. Open-source models like BGE and E5 offer privacy and […]
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