Revolutionizing Query Resolution with a RAG System Assisted by Agents

  • IntermediateLevel

  • 300+Students Enrolled

  • 30 MinsDuration

  • 4.6Average Rating

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About this Course

  • Learn what agentic RAG means and how this approach upgrades traditional RAG systems for accurate query resolution.
  • Understand how to create RAG with agents and automate complex query flows using intelligent orchestration.
  • Explore RAG agent architecture and learn how individual agents coordinate to produce reliable answers.
  • Gain hands-on experience in building AI agents with RAG and deploying them for real-world use cases.
  • Learn how to make a RAG agent with structured workflows, vector search, context retrieval, and LLM reasoning.

Learning Outcomes

Agentic RAG Fundamentals

Learn how agentic RAG systems combine retrieval & AI agents for answer

Build RAG Agents

Understand RAG agents with structured retrieval and reasoning paths.

Real-world Implementation

Build practical RAG with agents and deploy query-resolution workflows.

Who Should Enroll

  • Developers learning to build agentic RAG systems with optimized retrieval and reasoning.
  • Engineers exploring RAG with agents to improve accuracy and reliability in AI workflows.
  • Students who want to understand how to make a RAG agent from scratch.
  • Professionals looking to learn AI agents with RAG for enterprise-level automation.

Course Curriculum

Learn agentic RAG foundations: retrieval pipelines, query routing, and AI agent orchestration. Understand RAG architectures, build step-by-step agents, and deploy systems that boost accuracy in real-world query resolution.

toolstoolstools

  1. 1. Learn RAG based system

  2. 2. Hands-on: RAG based system

  3. 3. Simplified RAG Architecture

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Apoorv Vishnoi

Head - Training, Analytics Vidhya

Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups and mentored start-ups at incubation cells.

Get this Course Now

With this course you’ll get

  • 30 Mins

    Duration

  • Apoorv Vishnoi

    Instructor

  • Intermediate

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

Agentic RAG is an upgraded version of the retrieval augmented generation approach where multiple AI agents coordinate to improve accuracy, reliability, and context understanding. Instead of relying on a single model, agentic RAG uses specialized agents that retrieve, verify, refine, and generate answers. This leads to more consistent results and better query resolution in real-world applications.

RAG with agents ensures that retrieval, reasoning, validation, and response generation are handled by separate intelligent units. This decentralized process reduces hallucinations, ensures relevant context selection, and makes it easier to validate responses. It also allows systems to scale better because each agent specializes in one stage of the workflow.

You will learn how to design a RAG agent architecture where retrieval agents, evaluation agents, and generation agents work together. Each component plays a specific role, and the architecture helps maintain accuracy, interpretability, and control over the entire process.

No. The course is beginner friendly and explains the concepts clearly, including retrieval, embedding search, context preparation, and coordination between agents. All modules are structured to help both beginners and experienced developers understand how agentic RAG works.

Yes. The course guides you through the complete workflow. You will learn how to make a RAG agent by designing the retrieval pipeline, implementing the agent logic, integrating LLM reasoning, and evaluating outputs. This prepares you to build more advanced systems later.

AI agents are used for retrieval, ranking, verification, summarization, and final answer generation. You will learn how these agents collaborate, share context, and produce reliable outputs. The course also teaches when to delegate tasks to specific agents for better optimization.

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