InfoWorld AI·
For all their technical capabilities, large language models (LLMs) still have a memory problem. They can lack the ability to retain context across conversations, and don’t always contain the frameworks to let them access relevant data, ultimately making their results unreliable and untrustworthy. NoSQL database pioneer MongoDB is taking on this problem, releasing new persistent memory, retrieval, embedding, and re-ranking features, all integrated into one platform. The company is also introducing new security connectivity, open-source plugins, and other framework integrations to support agentic AI workloads. Supporting agentic memory “Unlocking the power of agents requires memory,” Pete Johnson, MongoDB’s field CTO of AI, said during a press briefing. “Just like human memory, a good agentic memory organizes knowledge. It helps agents retrieve the right knowledge based on context and learn to make smarter decisions and take optimized actions over time.” To advance automated retrieval an
Read full articleYour RAG system isn’t failing at retrieval — it’s failing at reasoning. This article shows how I built a lightweight self-healing layer that detects and corrects hallucinations before they reach users. The post RAG Hallucinates — I Built a Self-Healing Layer That Fixes It in Real Time appeared first on Towards Data Science.
Retrieval is where most RAG systems quietly break. Traditional pipelines rely on vector similarity—embedding queries and document chunks into the same space and fetching the “closest” matches. But similarity is a weak proxy for what we actually need: relevance grounded in reasoning. In long, professional documents—like financial reports, research papers, or legal texts—the right answer […] The post RAG Without Vectors: How PageIndex Retrieves by Reasoning appeared first on MarkTechPost.
Most RAG tutorials focus on retrieval or prompting. The real problem starts when context grows. This article shows a full context engineering system built in pure Python that controls memory, compression, re-ranking, and token budgets — so LLMs stay stable under real constraints. The post RAG Isn’t Enough — I Built the Missing Context Layer That Makes LLM Systems Work appeared first on Towards Data Science.
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