Agentic Rag Based Study Assistant For Devops And Mlops Concept

Abstract

DevOps And MLOps Have Become Essential Skills, But Students Often Struggle To Navigate Large Amounts Of Unstructured Learning Material Such As Documentation, Blogs, And Tutorials. This Project Proposes An Agentic RAG-based Study Assistant That Helps Learners Understand DevOps And MLOps Concepts Through Domain-grounded Question Answering And Personalized Study Support. The System First Builds A Knowledge Base By Collecting And Chunking Trusted DevOps/MLOps Resources, Then Converts Them Into Embeddings And Stores Them In A Vector Database Using A Retrieval-Augmented Generation (RAG) Pipeline. On Top Of This, Multiple AI Agents Are Orchestrated: A Retrieval Agent That Selects Relevant Content, A Planner Agent That Creates Topic-wise Study Plans, And A Tutor Agent That Provides Explanations, Summaries, And Practice Questions. By Constraining Responses To Retrieved, Curated Material, The Assistant Aims To Reduce Hallucinations And Improve Factual Accuracy For Educational Us.

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