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  • GuidesFeatured image showing a production RAG architecture using pgvector, LangChain, hybrid retrieval, BM25 keyword search, reranking, and LLM generation workflows.

    How to Build a RAG System with pgvector and LangChain: The Production Architecture

    How to Build a RAG System with pgvector and LangChain: The Production Architecture Most production AI failures are not model failures. They are retrieval failures. If you want to understand why your RAG system is hallucinating, stop looking at your prompt. A perfect prompt with the wrong data yields a confident hallucination. An average prompt with the correct data yields…

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  • BlogContext Engineering Explained featured image showing AI retrieval pipeline with retriever, reranker, context filter, and LLM workflow architecture.

    What Is Context Engineering?

    What Is Context Engineering? Why Prompt Engineering Is No Longer Enough Most production AI failures are not model failures. They are retrieval failures. For the last two years, the internet was flooded with “Prompt Engineering Cheat Sheets,” as if knowing how to tell an LLM to “take a deep breath” was a technical moat. Typing instructions into a chat box…

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  • BlogFuturistic illustration comparing vector databases and SQL databases for AI search systems, showing semantic vector networks, structured relational databases, and hybrid retrieval infrastructure with Digitpatrox branding.

    Vector Databases vs SQL Expained: What Actually Breaks in AI Search

    Vector Databases Explained: What Actually Breaks in Production A lot of teams assume semantic search “just works” once the embeddings are in place. It usually doesn’t. A year ago, every AI architecture diagram suddenly started including a vector database-Pinecone, Weaviate, Qdrant, Milvus. For a while, it felt like you needed one just to be taken seriously. In reality, most companies…

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