Improving Real World RAG Systems :Key Challenges
IntermediateLevel
2295+Students Enrolled
1 HrDuration
4.8Average Rating
About this Course
- 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.
- Through hands-on demos, you will gain insights into better chunking, embedding models, and agentic RAG systems.
Learning Outcomes
RAG Systems
Master RAG systems with a solid grasp of architecture.
Key Challenges
Solve key challenges like missing content and hallucinations.
Optimize Performance
Optimize performance with advanced strategies and solve challenges.
Course Curriculum
AI/ML practioners aiming to enhance RAG system. Enhance your skills in building and optimizing RAG systems with hands-on.
1. Introduction to RAG Systems
2. RAG System Challenges Practical Solutions
3. Hands-on: Solution for Missing Content in RAG
4. Other Key Challenges
5. Practical Solutions
6. Hands-on: Solution for Missed Top Ranked, Not in Context, Not Extracted
7. Wrong Format Problem Solution
8. Hands-on: Solution for Wrong Format
9. Incomplete Problem Solution
10. understanding HyDE
11. Other Practical Solutions from recent Research Papers
Get this Course Now
With this course you’ll get
- 1 Hour
Duration
- Dipanjan Sarkar
Instructor
- Intermediate
Level
Certificate of completion
Earn a professional certificate upon course completion
- Globally recognized certificate
- Verifiable online credential
- Enhances professional credibility
Frequently Asked Questions
Looking for answers to other questions?
A basic understanding of Al/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.
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.
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.
Unlike general Al/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.
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.
Yes, you will receive a certificate of completion after successfully finishing the course and assessments.
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