• 1 Hour

  • 4.8/5

  • Beginner

Course Description

This course explores the key challenges in building real-world Retrieval-Augmented Generation (RAG) systems and provides practical solutions. Topics include improving data retrieval, dealing with hallucinations, context selection, and optimizing system performance using advanced prompting, retrieval strategies, and evaluation techniques. Through hands-on demos, you will gain insights into better chunking, embedding models, and agentic RAG systems for more robust, real-world applications.

Course curriculum

  • 1
    Improving Real World RAG System
    • Introduction to RAG Systems
    • Resources
    • RAG System Challenges Practical Solutions
    • Hands-on: Solution for Missing Content in RAG
    • Other Key Challenges
    • Practical Solutions
    • Hands-on: Solution for Missed Top Ranked, Not in Context, Not Extracted _ Incorrect SpecificityHands-on- Solution for Missed
    • Wrong Format Problem Solution
    • Hands-on: Solution for Wrong Format
    • Incomplete Problem Solution
    • HyDE
    • Other Practical Solutions from recent Research Papers

Certificate of Completion

Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
Certificate of Completion

Who should Enroll?

  • AI/ML professionals aiming to enhance RAG system performance and solve real-world challenges.

  • Developers/Engineers building search, conversational, or generative AI systems needing better data retrieval and context handling.

  • Researchers/Enthusiasts seeking hands-on experience with advanced RAG techniques and agentic systems.

About the Instructor

Dipanjan Sarkar - Head of Community and Principal AI Scientist, Analytics Vidhya

Dipanjan Sarkar is a distinguished Lead Data Scientist, Published Author, and Consultant, having a decade of extensive expertise in Machine Learning, Deep Learning, Generative AI, Computer Vision, and Natural Language Processing. His leadership spans Fortune 100 enterprises to startups, crafting end-to-end data products and pioneering Generative AI upskilling programs. A seasoned mentor, Dipanjan advises a diverse clientele, from novices to C-suite executives and PhDs, across Advanced Analytics, Product Development, and Artificial Intelligence. His recognitions include "Top 10 Data Scientists in India, 2020," "40 under 40 Data Scientists, 2021," "Google Developer Expert in Machine Learning, 2019," and "Top 50 AI Thought Leaders, Global AI Hub, 2022," alongside global accolades and a Google Champion Innovator title in Cloud AI/ML, 2022.
LinkedIn
About the Instructor

Frequently Asked Questions (FAQs)

  • What prior knowledge is required?

    A basic understanding of AI/ML principles is needed, along with some experience in machine learning frameworks such as PyTorch or TensorFlow. Familiarity with natural language processing (NLP) concepts will be helpful but not mandatory.

  • Will there be hands-on exercises?

    Yes, the course provides practical, hands-on exercises through demos and notebooks. You’ll have opportunities to implement RAG systems and experiment with real-world use cases, focusing on improving retrieval and generation tasks.

  • What tools or software will I need?

    You’ll need access to Python, Jupyter Notebooks, and relevant libraries such as LangChain, Hugging Face, and vector databases. The course will guide you through setting up the necessary environment for practicing the techniques.

  • How is this course different from other AI courses?

    Unlike general AI/ML courses, this course zeroes in on Retrieval-Augmented Generation (RAG) systems, addressing practical challenges like hallucinations, retrieval errors, and context optimization, with a strong emphasis on real-world applications.

  • Will I learn advanced techniques?

    Yes, the course covers advanced techniques like hyperparameter tuning, chunking strategies, embedding models, context compression, and agentic RAG systems, giving you the tools to build and optimize high-performing RAG systems.

Key Takeaways

  • Master RAG systems with a solid grasp of architecture and components.

  • Solve key challenges like missing content and hallucinations.

  • Optimize performance with advanced chunking and retrieval strategies.

  • Develop practical decision-making skills for LLM adoption in various industries.