The Algorithm That’s Dumb by Name but Smart by Nature: Naive Bayes
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. A no-nonsense guide to understanding one of the most battle-tested ML algorithms out there I’ll be honest with you — when I first heard “Naive Bayes,” I thought, how good can an algorithm …
Bagging vs Boosting: The Complete Beginner’s Guide to Ensemble Learning in Machine Learning
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. From decision trees to Random Forest and XGBoost — everything explained simply, with real-life analogies, examples, and code. You’ve probably heard the saying: “Two heads are better than one.” After the introduction, the …
What Really Makes Cars Pollute? A Data Science Deep Dive into CO₂ Emissions
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. How I built a 98.8% accurate prediction model — and discovered that the “cleanest” fuel is hiding a dirty secret When the Global Automotive Council wants to reduce vehicle emissions, where do they …
Graph Databases for Cloud Security Posture Management (CSPM)
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. Cloud infrastructure is a dynamic, sprawling landscape. As organizations embrace multi-cloud and hybrid environments, managing security becomes a complex, multi-dimensional challenge. Traditional security tools often struggle to provide the context needed to understand …
Understanding LLM Sampling: Top-K, Top-P, and Temperature
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. Mastering Creativity and Control with Temperature, Top-K, and Top-P LLM sampling is how a model decides the next word to generate from a list of possibilities. Rather than simply picking the most likely …
Unlock True AI Understanding: Beyond RAG with Knowledge-Augmented Generation (KAG)
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. Tired of AI Hallucinations? Discover How KAG Delivers Professional-Grade Accuracy for Your Domain. In the rapidly evolving world of AI, we’re constantly seeking ways to make our systems smarter, more reliable, and truly …
Why Most RAG Pipelines Fail (And How to Fix Them)
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. From chunking to retrieval to evaluation, here’s how to turn RAG from demo to production-ready. Ever built a Retrieval Augmented Generation (RAG) pipeline that shined in demo but crumbled in production? You’re not …
Adaptive RAG: The Smart, Self-Correcting Framework for Complex AI Queries
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. Introduction: Why Adaptive RAG is a Game-Changer for AI Retrieval When you ask your AI assistant a question, have you ever wondered how it decides whether to answer quickly from its memory or …
Corrective RAG: How to Build Self-Correcting Retrieval-Augmented Generation
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. Corrective RAG: How to Build Self-Correcting Retrieval-Augmented Generation Retrieval-Augmented Generation (RAG) has completely transformed how we build Large Language Model (LLM) applications. It gives LLMs the superpower to fetch external knowledge and generate …
How to Build Agentic RAG: A Step-by-Step Guide to Intelligent Retrieval-Augmented GenerationTaking Retrieval-Augmented Generation to the Next Level with Intelligent Agents
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. Using interrupt and conditional routing, escalate a request to a human expert If you’ve worked with Retrieval-Augmented Generation (RAG), you know it’s a game-changer for enhancing Large Language Models (LLMs) by fetching relevant …
Human-in-the-Loop (HITL) with LangGraph: A Practical Guide to Interactive Agentic Workflows
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. Introduction In the rapidly evolving landscape of AI agents and autonomous systems, human-in-the-loop (HITL) workflows are becoming increasingly crucial. They bring the perfect balance between automation and human oversight, enabling safer, smarter, and …
A Complete Guide to Multi-Agent Systems in LangGraph: Network to Supervisor and Hierarchical Models
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. A Complete Guide to Multi-Agent Systems in LangGraph: Network to Supervisor and Hierarchical Models In modern AI applications, we often expect systems to handle complex, multi-step tasks. Instead of relying on a single …
Building Your Own MCP Servers: A Step-by-Step Guide using MultiServerMCPClient
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. Unlock the Power of Model Context Protocol (MCP) for AI Applications Have you ever wanted to integrate custom tools — like weather APIs, or third-party services — into your AI applications seamlessly? The …
Building Agentic Workflows with LangGraph: A Deep Dive into ReAct and Memory Management (Part 1)
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. Building Agentic Workflows with LangGraph: A Deep Dive into ReAct and Memory Management (Part 1) In the world of AI and LLMs, agentic workflows are revolutionizing how we interact with AI systems. These …
Building Smart Agents: LangGraph + Perplexity with Memory for Developers
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. A Hands-On Guide to Creating Intelligent AI Agents with Persistent Memory using LangGraph and Perplexity AI. Hey fellow developers and AI enthusiasts! Have you heard the buzz? Perplexity AI is offering Pro membership …
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