Learning Data Science and Sharing the journey through Hashnode.
Full-time Internship
Jan 16 · 9 min read · Introduction In Part 1, we built a RAG agent that can think and fix its own search queries. In Part 2, we gave it structured memory using hierarchical indexing and hybrid storage. We now have a capable researcher. It can handle vague questions. It ca...
Join discussion
Dec 24, 2025 · 6 min read · Introduction In Part 1, we gave our RAG system a brain. We built an agent that can analyze queries, ask for clarification, and fix its own mistakes using LangGraph. But even the smartest agent is useless if it’s reading from a messy library. Today, w...
Join discussion
Dec 24, 2025 · 6 min read · Introduction Retrieval-Augmented Generation (RAG) has become the "Hello World" of modern GenAI development. It is the standard way to ground Large Language Models (LLMs) in your own data. But let’s be honest—most RAG pipelines are "dumb." They are fr...
Join discussion
Nov 29, 2025 · 6 min read · Introduction In the era of remote and hybrid work, meetings have become the backbone of collaboration. But they also generate massive amounts of unstructured data. What if you could query your meeting history like a database, extract action items, or...
Join discussion
Jan 5, 2025 · 13 min read · Introduction In the last three blogs in our Ultimate Langraph Tutorial Series, we highlighted different components of LangGraph for beginners, Long-term Memory Support, and building an AI agent with custom tools support. After implementing these syst...
Join discussion