BIBhushan Ingoleinrag-concepts.hashnode.dev·Aug 22, 2025 · 3 min readAdvanced RAG Concepts: Building Scalable, Accurate, and Production-Ready SystemsRetrieval-Augmented Generation (RAG) has become one of the most powerful ways to enhance large language models (LLMs). By grounding outputs in external knowledge bases, RAG reduces hallucinations and improves factual accuracy. But as systems move fro...00
BIBhushan Ingoleinwhatt-is-rag.hashnode.dev·Aug 20, 2025 · 2 min readWhere RAG failsAs we what is RAG it the combination of technique which are LLM and information retrieval which gives more strength to the LLM So Let me Enlighten you by telling you the LIMITATIONS of RAG As we know there are two PHASES OF RAG in which it fails ~ Re...00
BIBhushan Ingoleinwhatt-is-rag.hashnode.dev·Aug 19, 2025 · 2 min readWhat is RAG ApplicationRAG is a technique that combines the power of information retrieval with the creativity of generative language models ( LLM ). By applying this technique, the model becomes more powerful, as it can access data from a database and also store data in a...00
BIBhushan Ingoleinagenticai-explain.hashnode.dev·Aug 18, 2025 · 1 min readWhat if When Systems Start Thinking for ThemselvesYou know about traditional AI until now, but as you may have noticed, it cannot provide real-time data. For example, if you ask about the current weather in your city, it won’t be able to answer because traditional AI works on past data, trained from...00
BIBhushan Ingoleinbuild-thinkingg-model.hashnode.dev·Aug 15, 2025 · 3 min readBuilding a Thinking Model from a Non-Thinking Model Using Chain-of-ThoughtIn the world of AI, not all models are “thinkers” by default. Many large language models (LLMs) can generate fluent answers, but they don’t always reason through a problem step-by-step. This can lead to mistakes, especially with complex tasks like ma...00
