Introduction: Initial retrieval casts a wide net—vector search or keyword matching returns candidates that might be relevant. Reranking narrows the focus, using more expensive but accurate models to score each candidate against the query. Cross-encoders process query-document pairs together, capturing fine-grained semantic relationships that bi-encoders miss. This two-stage approach balances efficiency with accuracy: fast retrieval […]
Read more →Category: Artificial Intelligence(AI)
Semantic Search Optimization: Building High-Quality Retrieval Systems
Introduction: Semantic search goes beyond keyword matching to understand the meaning and intent behind queries. By converting text to dense vector embeddings, semantic search finds conceptually similar content even when exact words don’t match. However, naive implementations often underperform—poor embedding choices, suboptimal indexing, and lack of reranking lead to irrelevant results. This guide covers practical […]
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 Fine-Tuning: Training Custom Embeddings for Domain-Specific Retrieval
Introduction: Off-the-shelf embedding models work well for general text, but domain-specific applications often need better performance. Fine-tuning embeddings on your data can dramatically improve retrieval quality—turning a 70% recall into 90%+ for your specific use case. The key is creating high-quality training data that teaches the model what “similar” means in your domain. This guide […]
Read more →RAG Patterns: Advanced Retrieval Augmented Generation Strategies
Introduction: Retrieval Augmented Generation (RAG) has become the standard pattern for grounding LLM responses in factual, up-to-date information. But basic RAG—retrieve chunks, stuff into prompt, generate—often falls short in production. Queries get misunderstood, irrelevant chunks pollute context, and answers lack coherence. This guide covers advanced RAG patterns that address these challenges: query transformation to improve […]
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 […]
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