Articles tagged “RAG”
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5 min readBest Vector Database 2026: Pinecone vs Qdrant vs Weaviate vs pgvector
A 2026 guide to picking a vector database — Pinecone, Qdrant, Weaviate, or pgvector. Pricing, performance, hybrid search, and which workload fits which engine.
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6 min readHallucination Detection: Techniques That Actually Work
A 2026 guide to LLM hallucination detection — grounding verification, self-consistency, classifier-based detection, and how to stack techniques in production.
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6 min readTutorial: Build a Production RAG App in 2 Hours
A practical 2026 RAG tutorial — chunking, hybrid retrieval, reranking, citations, and eval. Production-grade Python code for OpenAI, Qdrant, Cohere.
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5 min readEmbedding Models in 2026: OpenAI vs Cohere vs Open Source
A 2026 embedding model comparison — text-embedding-3, voyage-3, Cohere embed-v3, BGE-M3, Google — with quality, dimensions, context, and cost tradeoffs.
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6 min readFine-Tuning vs RAG: The 2026 Decision Framework
A 2026 framework for choosing between fine-tuning and RAG — what each does, when each wins, and the hybrid pattern that most production systems actually use.
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6 min readRAG Best Practices in 2026: Chunking, Reranking, Hybrid Search
The 2026 RAG playbook — chunking strategies, hybrid retrieval, rerankers, and how long context fits in. Practical defaults and the four levers that move quality.
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5 min readKnowledge Graphs vs Vector RAG: When to Use Which in 2026
A practical comparison of knowledge graph RAG and vector RAG in 2026 — how they work, when to use each, and hybrid architectures.
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