Learn AI

AI Deep Dive

Category: RAG

  • AWS AI-Powered Coding Tools

    AWS AI Coding Tools Amazon Web Services (AWS) offers a comprehensive suite of AI-powered coding tools that leverage machine learning to assist developers throughout the software development lifecycle. These services aim to enhance productivity, improve code quality, and automate complex tasks, from code generation to MLOps. 1. Amazon CodeWhisperer Amazon CodeWhisperer is a machine learning Read more

  • Google’s AI-Powered Coding Tools

    Google AI Coding Tools Google provides a powerful suite of AI-driven coding tools, primarily leveraging its advanced AI models like Gemini, to assist developers throughout the software development lifecycle. These tools are designed to boost productivity, improve code quality, and automate routine tasks, making coding more efficient and accessible. 1. Jules: Your Asynchronous AI Coding Read more

  • Agentic AI: The Critical Role of Explainable AI (XAI)

    Agentic AI: The Critical Role of Explainable AI (XAI) Agentic AI promises a significant evolution in how artificial intelligence systems operate, enabling autonomous, intelligent, and adaptive behavior. However, the full potential and responsible deployment of these powerful systems hinge on our ability to understand their decision-making processes. This is where Explainable AI (XAI) becomes not Read more

  • Agentic AI for Business Process Management (BPM): A Detailed Exploration

    Agentic AI for Business Process Management (BPM): A Detailed Exploration Agentic AI represents a significant evolution in Business Process Management (BPM), promising a new level of autonomy, intelligence, and adaptability to how organizations manage their workflows. Understanding Agentic AI Agentic AI refers to artificial intelligence entities capable of perceiving, reasoning, acting, and learning autonomously to Read more

  • Exploring Graph Databases vs Vector Databases: A Detailed Comparison

    Exploring Graph Databases vs Vector Databases: A Detailed Comparison This document provides an in-depth exploration of graph databases and vector databases, highlighting their core concepts, functionalities, and architectural considerations to help you choose the right tool for your data needs. Graph Databases: Unraveling the Fabric of Connected Data Core Concepts Nodes (Vertices): Represent entities with Read more

  • Retrieval-Augmented Generation (RAG) Enhanced by Model Context Protocol (MCP)

    RAG Enhanced by MCP: Detailed Explanation The integration of Retrieval-Augmented Generation (RAG) with the Model Context Protocol (MCP) offers a powerful paradigm for building more intelligent and versatile Large Language Model (LLM) applications. MCP provides a structured way for LLMs to interact with external tools and data sources, which can significantly enhance the retrieval capabilities Read more

  • Various flavors of Retrieval Augmented Generation (RAG)

    Various Types of RAG The field of Retrieval-Augmented Generation (RAG) is rapidly evolving, with several variations and advanced techniques emerging beyond the basic “naive” RAG. I. Based on the Core RAG Pipeline 1. Naive/Standard RAG The user’s query is directly used to retrieve relevant documents, and these are passed to the LLM for generation. Use Read more

  • Understanding Agentic Retrieval-Augmented Generation (RAG)

    Understanding Agentic RAG Agentic Retrieval-Augmented Generation (RAG) goes beyond standard RAG by incorporating more sophisticated agent-like behaviors to enhance the generation process. Think of it as a proactive and strategic assistant for information retrieval and content generation. Key Differences from Standard RAG Decision-Making in Retrieval: Agentic RAG decides *when* and *how* to retrieve information, unlike Read more

  • Implementing Locally running Mistral Chatbot with RAG

    Locally running Mistral Chatbot with RAG Let’s implement a local running chatbot with Mistral LLM using RAG to retrieve documents from a locally running Vector DB that also contains FAQs. Here’s a breakdown of the steps and the Python code to achieve this: Phase 1: Setting Up the Local Environment Install Dependencies: pip install transformers Read more

  • Top 10 LLMs on Hugging Face for Chatbot & RAG Use (Early May 2025)

    Top 10 LLMs on Hugging Face for Chatbot & RAG This list is based on a combination of factors including general popularity, instruction-following capabilities, context window size, and community interest relevant to chatbot and Retrieval-Augmented Generation (RAG) applications. 1. mistralai/Mixtral-8x7B-Instruct-v0.1 Use Cases: Excellent for instruction following, complex reasoning in chatbots, and can handle long contexts Read more