ASAditya Singhinbytebeam.hashnode.dev·Aug 20, 2025 · 4 min readUnderstanding and Solving the Challenges of Retrieval Augmented Generation (RAG)Retrieval Augmented Generation (RAG) systems promise context-aware, up-to-date answers by combining large language models (LLMs) with information retrieval. But while powerful, RAG can suffer from subtle and impactful failure modes that reduce accura...00
ASAditya Singhinbytebeam.hashnode.dev·Aug 20, 2025 · 4 min readRetrieval Augmented Generation (RAG): Bridging Knowledge Gaps in AI Language ModelsIntroduction to Retrieval Augmented Generation (RAG) Retrieval Augmented Generation (RAG) is a cutting-edge natural language processing (NLP) technique that combines the best of both worlds: retrieval-based systems and generative models. RAG architec...00
ASAditya Singhinbytebeam.hashnode.dev·Aug 18, 2025 · 3 min readAgentic AI: Agents and Tools ExplainedAgentic AI refers to advanced artificial intelligence systems composed of autonomous, adaptive agents designed to achieve specific goals with minimal human intervention. Unlike traditional or generative AI, which often execute predefined tasks or gen...00
ASAditya Singhinbytebeam.hashnode.dev·Aug 15, 2025 · 3 min read🧠Building a Thinking Model from a Non-Thinking Model Using Chain-of-Thought PromptingMost AI models don’t “think.” They predict. Given a prompt, they generate the next token based on probability—not logic. So how do we get a non-thinking model to behave like a reasoning agent? Enter Chain-of-Thought (CoT) prompting—a deceptively simp...00
ASAditya Singhinbytebeam.hashnode.dev·Aug 15, 2025 · 4 min read🧠Understanding System Prompting: The Backbone of AI BehaviorIn the world of AI assistants and LLM-powered apps, prompting isn’t just a way to ask questions—it’s how we shape intelligence. Whether you're building a chatbot, a coding assistant, or an educational tutor, the system prompt is your blueprint for be...00
