Improving Real World RAG Systems :Key Challenges

IntermediateLevel
2094+Students Enrolled
1 HrDuration
4.8Average Rating

About
Learning Outcomes
Who Should Enroll
Course Curriculum
Instructors

About this Course

This course explores key challenges in building real-world RAG systems & teaches practical solutions to improve retrieval, reduce hallucinations, and enhance context selection.
Learn how to strengthen real-world RAG systems by improving data retrieval, fixing hallucinations, refining context windows, & boosting system efficiency using advanced techniques.
Gain practical skills to build advanced RAG applications with better chunking, stronger embedding models, and agentic RAG workflows. Learn how to solve real-world issues.

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.

Who Should Enroll

Developers & data scientists building real-world RAG applications and seeking to improve system accuracy & reliabilily.
ML engineers aim to master advanced retrieval-augmented generation techniques for large-scale production use.
Professionals want to learn hands-on to fix retrieval issues, reduce hallucinations, & optimize end-to-end RAG systems.

Course Curriculum

Learn to build a QA RAG system with LangChain, covering data ingestion, embeddings, vector stores, retrieval pipelines, prompt design, evaluation, and deployment.

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

Meet the instructor

Our instructor and mentors carry years of experience in data industry

Dipanjan Sarkar

Principal AI Scientist

Dipanjan Sarkar, Head of AI, Author & Consultant, has 12+ years of expertise in ML, DL, GenAI, CV & NLP. He has led AI initiatives across Fortune 100 firms & startups, building data products & upskilling professionals at all levels.

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

Industry-Recognized Credential
Career Advancement Credential
Shareable Achievement

Frequently Asked Questions

Looking for answers to other questions?

Real-world RAG systems face issues like noisy data, weak retrieval quality, hallucinations, context limits, and performance bottlenecks. These challenges require careful design, evaluation, and optimization across retrieval, ranking, chunking, and LLM reasoning layers.

RAG performance directly depends on the relevance of retrieved context. Poor retrieval leads to hallucinations, incorrect answers, and inconsistent behavior. Strengthening chunking, embeddings, indexing, and ranking dramatically improves system accuracy and reliability.

Hallucinations arise when the model lacks correct context, receives irrelevant documents, or encounters ambiguous user queries. They can also result from weak prompting or insufficient guardrails. Improving retrieval and context grounding significantly reduces them.

Chunking determines how information is broken and stored in your vector database. If chunks are too large or too small, retrieval fails. Effective chunking improves semantic relevance, reduces noise, and boosts the model’s ability to find the right facts.

Embedding models convert text into vectors for similarity search. Higher-quality embeddings capture meaning more accurately, improving retrieval relevance. Choosing the right embedding model directly impacts precision, recall, and overall RAG system reliability.

Agentic RAG enhances traditional RAG by adding reasoning steps, routing, tool usage, and self-correction. It helps systems handle complex tasks, validate responses, and iteratively improve output quality, making RAG more robust for real-world applications.

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