RSritesh sharmainrs24.hashnode.dev·Aug 22, 2025 · 6 min readRAG Concepts for Production-Ready SystemsRetrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for building knowledge-intensive applications by grounding Large Language Models (LLMs) in external data. While basic RAG pipelines are relatively straightforward, deploying robu...00
RSritesh sharmainrs24.hashnode.dev·Aug 20, 2025 · 4 min readThe Rise of Agentic AI: When LLMs Become More Than Just Clever ChatbotsLarge Language Models (LLMs) have taken the world by storm, but let's be honest, they've been mostly "reactive." You give them a prompt, and they give you a response. They're like brilliant but lazy interns they can do amazing things, but only if you...00
RSritesh sharmainrs24.hashnode.dev·Aug 20, 2025 · 5 min readWhen Your RAG System Gets a Brain Freeze: Dealing with Hallucinations, Poor Context, and MoreAh, Retrieval-Augmented Generation (RAG). The knight in shining armor promising to banish those pesky LLM hallucinations and keep our AI overlords factually grounded. Except, sometimes our valiant knight trips over its own (badly chunked) feet. Let's...00
RSritesh sharmainrs24.hashnode.dev·Aug 20, 2025 · 4 min readRetrieval-Augmented Generation (RAG): Your Guide to Smarter LLMsLarge Language Models (LLMs) are powerful, but they have a major limitation: they can only access the information they were trained on. This is where Retrieval-Augmented Generation (RAG) comes in. RAG is a technique that combines the power of a retri...00
RSritesh sharmainrs24.hashnode.dev·Aug 14, 2025 · 5 min readFrom Parrot to Problem-Solver: Teaching an LLM to "Think" with Chain-of-ThoughtLarge Language Models (LLMs) are incredible. They can write code, draft emails, and answer questions on almost any topic. But sometimes, they fail in ways that are bafflingly simple. You ask a logic question, and the AI gives you a confident, well-wr...00