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Category: RAG

  • The Rise of Small Language Models (SLMs): Challenges and Mitigations

    The Rise of Small Language Models (SLMs): Challenges and Mitigations The field of Artificial Intelligence is experiencing a significant shift, with Small Language Models (SLMs) emerging as a powerful and practical alternative to their larger counterparts, Large Language Models (LLMs). While LLMs like GPT-4 have showcased remarkable general capabilities, the practical challenges and limitations associated Read more

  • Powering Intelligence: Understanding the Electricity and Cost of 1 Million RAG Queries

    Powering Intelligence: Understanding the Electricity and Cost of 1 Million RAG Queries for Solution Architects As solution architects, you’re tasked with designing robust, scalable, and economically viable AI systems. Retrieval-Augmented Generation (RAG) has emerged as a transformative pattern for deploying large language models (LLMs), offering a compelling alternative to continuous fine-tuning by grounding responses in Read more

  • Vector Databases vs. MongoDB: Storing & Finding Data (Multi Modal Embedded Data) – A Master’s Guide

    Vector DBs vs. MongoDB: Storing & Finding Data – A Master’s Guide In the rapidly evolving landscape of AI and data, a new type of database has emerged: the Vector Database. While MongoDB excels at storing and querying diverse, semi-structured documents, Vector DBs are purpose-built for a very specific, yet increasingly critical, type of data: Read more

  • Agentic AI Workflow Tutorial for Beginners: Building a Smart Customer Service Assistant

    Agentic AI Workflow Tutorial for Beginners (Expanded) Welcome to the exciting world of Agentic AI! This expanded tutorial will delve deeper into the core concepts and provide more detailed explanations for each component, including illustrative (but not executable) code snippets and conceptual datasets. We’ll continue with our goal of building a basic Smart Customer Service Read more

  • Mastering LangChain and LangGraph: From Novice to Expert

    Mastering LangChain and LangGraph: From Novice to Expert You’re about to become an expert in building powerful AI applications using LangChain and LangGraph. These two frameworks are essential tools for anyone looking to go beyond simple prompts and create sophisticated, intelligent systems powered by Large Language Models (LLMs). We’ll start with the fundamentals of LangChain, Read more

  • Mastering Mosaic AI Vector Search: From Novice to Expert

    Mastering Mosaic AI Vector Search: From Novice to Expert You’re about to embark on a journey from understanding the basics of vector search to becoming an expert in leveraging Databricks’ powerful Mosaic AI Vector Search. This technology is at the heart of making AI truly intelligent, enabling Large Language Models (LLMs) and other AI systems Read more

  • Mosaic AI Agent Framework vs. LangGraph: A Detailed Comparison

    Mosaic AI Agent Framework vs. LangGraph: A Detailed Comparison When building sophisticated AI agents, developers often face a choice between general-purpose frameworks and platform-specific solutions. This comparison will delve into two prominent options: Databricks’ Mosaic AI Agent Framework and LangGraph (a module of LangChain), highlighting their strengths, weaknesses, and ideal use cases. Both frameworks aim Read more

  • Detailed Guide to Using Databricks with Agentic AI

    Detailed Guide to Using Databricks with Agentic AI Databricks, with its unified Lakehouse Platform, offers a robust environment for developing, deploying, and managing Agentic AI systems. Agentic AI involves AI models (often Large Language Models – LLMs) that can reason, plan, use tools, and take autonomous actions. This guide will detail how to leverage Databricks Read more

  • AI-Assisted Code Development & Validation Workflow: A Comprehensive Guide

    AI-Assisted Code Development & Validation Workflow This workflow outlines the systematic steps for developing software with the assistance of AI code generators, ensuring robust validation, security, and adherence to quality standards. It assigns clear roles and details the critical checks required at each stage. Workflow Summary: Key Tools & Links This workflow integrates various tools Read more

  • Steps Developers Need to Take to Trust and Validate AI-Generated Code

    Trusting and Validating AI-Generated Code – Detailed Guide While AI code generators offer significant productivity boosts, integrating their output into production systems requires a robust approach to trust and validation. Developers cannot blindly accept AI-generated code; instead, they must employ a series of rigorous steps to ensure its correctness, security, performance, and adherence to best Read more