Building and Evaluating RAG System
IntermediateLevel
800+Students Enrolled
2 Hrs Duration
4.6Average Rating
About this Course
- Explore the fundamentals of RAG technology by mastering innovative data ingestion, advanced text embedding, and efficient retrieval techniques to design robust pipelines.
- Deepen your expertise with state-of-the-art reranking methods and similarity search strategies while implementing both open-source and LLM-based evaluation techniques.
- Elevate your skills with advanced RAG architectures tailored for complex applications, focusing on scaling and optimizing systems.
Learning Outcomes
Master RAG fundamentals
Explore RAG fundamental: data ingestion, text embedding, and retrieval
Advanced Rerank
Master advanced reranking, similarity search, and LLM evaluations
Design RAG Architectures
Advance your skills with RAG architectures for scaling complex systems
Who Should Enroll
- AI professionals seeking to master advanced RAG fundamentals and system-level architecture.
- Data engineers optimizing data ingestion, embeddings, and retrieval workflows for RAG systems.
- Beginners looking to build practical, end-to-end RAG pipelines with hands-on evaluation skills.
Course Curriculum
Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.
1. Introduction to RAG
2. RAG-based Q&A system pipeline
3. Pain Points in RAG system
4. Evaluation Measured: Retrieval Metrics
1. Advanced RAG Techniques and Colpali RAG
2. Hands On: Colpali RAG
3. LangGraph Studio: Building,Tracing & Evaluating AI Agents
4. Hands On: GPT 4o Research Agent in LangGraph
Meet the instructor
Our instructor and mentors carry years of experience in data industry
Get this Course Now
With this course you’ll get
- 2 Hours
Duration
- Nikhil Pentapalli
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?
Retrieval-Augmented Generation (RAG) is an approach that integrates a retrieval mechanism with a generative model to enhance the quality and accuracy of generated content. It works by first retrieving relevant information from a large corpus or database, then using that data to inform and improve the output of the generative model.
The series covers innovative data ingestion, advanced text embedding, robust retrieval methods, and the construction and optimization of RAG pipelines.
RAG tackles issues like misinformation and context loss by ensuring outputs are supported by up-to-date, retrieved data.
Yes, the course provides a certification upon completion.
RAG offers the advantage of grounding responses in factual data, reducing errors common in pure generative models and providing more context than retrieval-only approaches.
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