#rag

Building a RAG system just got much easier. Google’s File Search tool for the Gemini API now handles the heavy lifting of connecting LLMs to your data. Chunking, embedding, indexing are all managed for you. And with the latest update, it’s gone multimodal. You can now search through both text and images in a single […] The post Gemini API File Search: The Easy Way to Build RAG appeared first on Analytics Vidhya.

Your 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.

Modern AI systems struggle with memory. They often forget past interactions or rely on Retrieval-Augmented Generation (RAG), which depends on constant access to external data. This becomes a limitation when building assistants that need both historical context and a deeper understanding of users. MemPalace offers a different approach, enabling structured, persistent memory with higher precision […] The post MemPalace Explained: Building Long-Term Memory for AI Agents Beyond RAG  appeared first on Analytics Vidhya.

A few years ago, most AI models ran out of context after a short conversation. Today, leading models hold one million tokens or more. This guide breaks down context length in LLMs, how tokens work, what the lost in the middle effect means for output quality, and when RAG outperforms long context.