Course Overview
Starting on Sunday, March 30th, 2025 at 11 AM PST
This comprehensive 8-week curriculum is meticulously designed to equip AI engineers with both theoretical knowledge and practical skills in Retrieval-Augmented Generation techniques. By engaging in hands-on projects and programming sessions, participants will not only understand the underlying concepts but also gain the experience necessary to implement and optimize RAG systems in real-world applications.
Emphasis on Practical Value: Each day’s agenda is structured to bridge the gap between theory and practice. Engineers will leave the course with a portfolio of projects, a deep understanding of advanced RAG methodologies, and the confidence to tackle complex challenges in the field of AI.
Learning Outcome
You will develop theoretical and hands-on expertise in Retrieval-Augmented Generation (RAG) techniques.
You will master building and optimizing RAG pipelines with large language models (LLMs).
Schedule
| Start Date | SUNDAY, MARCH 30th, 2025 |
|---|---|
| Periodicity | Meet every Sunday & Wednesday for six weeks |
| Schedule | From 11 AM to 5 PM PST |
| Morning Session | 11 AM to 1 PM PST |
| Lunch Served | 1 PM to 1.30 PM PST |
| Afternoon Session | 1.30 PM to 4 PM PST |
| Project Presentations | 4 PM to 5 PM |
| LAB Walkthrough | Wednesday, 7 PM to 10 PM |
Call us at 1.855.LEARN.AI for more information.
Skills You Will Learn
Core Knowledge of Retrieval-Augmented Generation (RAG), Data Processing and Management, Model Training and Fine-Tuning.
Prerequisites
The teaching faculty for this course comprises the instructor, a supportive staff of teaching assistants, and a course coordinator. Together, they facilitate learning through close guidance and 1-1 sessions when needed.
Syllabus Details
This curriculum spans six Sundays and six weeks of projects and labs. Each week has its theme, giving us time to explore it in considerable practical and foundational detail. The outcome of each week should be a reasonably expert-level proficiency in the topics at hand.
Morning Session:
- Introduction to RAG Fundamentals
- Overview of RAG Architecture: Understanding how retrieval mechanisms enhance language generation.
- Core Concepts: Differentiating between retrieval and generation components.
- Open-Source RAG Frameworks
- LlamaIndex and LangChain: Exploring features, capabilities, and use-cases.
- Hands-On Setup: Installing and configuring these frameworks for development purposes.
Afternoon Session:
- Evaluation Metrics for RAG Systems
- Meaningful Metrics: Precision, recall, F1-score, and their relevance in RAG.
- Performance Assessment: Techniques for evaluating both retrieval and generation quality.
- Practical Workshop
- Building a Basic RAG System: Using open-source frameworks to create a simple application.
- Experimentation: Tweaking parameters and observing effects on output quality.
Practical Value: Establishing a solid foundation equips engineers with the essential tools and understanding needed to develop sophisticated RAG systems. Familiarity with open-source frameworks accelerates the development process and encourages best practices.
Morning Session:
- Vector Databases and ANN-Indexing Approaches
- Understanding Approximate Nearest Neighbors (ANN): Techniques like HNSW, FAISS, and their algorithms.
- Performance vs. Efficiency Trade-offs: Balancing speed and accuracy in large-scale applications.
- Text Encoders
- Survey of Text Encoders: From traditional TF-IDF to transformer-based models like BERT.
- Architectures and Trade-offs: Comparing computational costs and performance metrics.
Afternoon Session:
- Re-Ranking with Cross-Encoders
- Hybrid Search Strategies: Combining keyword-based and vector embedding searches.
- Implementation Techniques: Enhancing retrieval results with cross-encoder models.
- Hands-On Lab
- Implementing Vector Databases: Setting up and querying with different ANN indexes.
- Encoder Comparison: Testing various text encoders and analyzing their impact on retrieval quality.
Practical Value: Mastery of retrieval components is crucial for building efficient RAG systems. Understanding the underlying mechanisms allows engineers to optimize performance and scalability according to specific project needs.
Morning Session:
- Effective Semantic Chunking Methods
- Techniques like Raptor and Content-Rewrite: Improving retrieval granularity and relevance.
- Chunking Strategies: Deciding on chunk sizes and overlap for optimal performance.
- COLBERT and Late-Interaction Methods
- Concepts of Late Interaction: How COLBERT enhances retrieval accuracy.
- Implementation Details: Setting up COLBERT models and integrating them into RAG systems.
Afternoon Session:
- RAG over Graphs, Figures, and Tables
- Non-Textual Data Retrieval: Techniques for handling structured and semi-structured data.
- Integration Strategies: Merging textual and non-textual data retrieval in RAG.
- Project Work
- Implementing Advanced Techniques: Applying semantic chunking and late-interaction methods to a RAG system.
- Evaluation and Optimization: Measuring improvements and refining the system.
Practical Value: Advanced retrieval techniques significantly enhance the capability of RAG systems to handle complex queries and diverse data types, which is essential for developing robust AI applications.
Morning Session:
- Query Transformation Techniques
- HyDE, Rewrite-Retrieve-Read, Step-Back Prompting: Methods to reformulate queries for better retrieval.
- RAG-Fusion: Combining multiple retrieval results for enriched generation.
- Knowledge-Graph-Based RAG Approaches
- Leveraging Knowledge Graphs: Enhancing context and relevance in responses.
- Integration Methods: Connecting knowledge graphs with retrieval and generation components.
Afternoon Session:
- Practical Implementation
- Applying Query Transformations: Developing modules that reformulate queries.
- Integrating Knowledge Graphs: Building a RAG system that utilizes knowledge graphs for improved results.
- Case Studies and Discussion
- Real-World Applications: Examining how companies use these techniques in production.
- Challenges and Solutions: Addressing common obstacles in implementation.
Practical Value: Query transformation and knowledge integration are pivotal for creating intelligent RAG systems that can understand and respond to user queries more effectively, thereby improving user satisfaction and system utility.
Morning Session:
- Multimodal Embedding Models
- Understanding Multimodality: Combining text, images, audio, and other data types.
- Model Architectures: Exploring models like CLIP that handle multimodal data.
- Personalization and User-Adaptive RAG
- Techniques for Personalization: User profiling, context awareness, and preference learning.
- Adaptive Algorithms: Methods for tailoring responses based on individual user data.
Afternoon Session:
- Hands-On Development
- Building Multimodal RAG Systems: Incorporating different data types into retrieval and generation.
- Implementing Personalization Features: Developing algorithms that adapt to user behaviors.
- Ethical Considerations
- Privacy and Data Security: Ensuring user data is handled responsibly.
- Bias Mitigation: Strategies to prevent biased outputs in personalized systems.
Practical Value: Incorporating multimodality and personalization makes RAG systems more versatile and user-friendly, which is essential in developing AI solutions that meet users’ diverse needs in real-world scenarios.
Morning Session:
- Fine-Tuning Models
- Embedding Models and LLMs: Techniques for adapting pre-trained models to specific tasks.
- Training Strategies: Transfer learning, domain adaptation, and hyperparameter tuning.
- Efficient Data Ingestion Pipelines for RAG
- Handling Large-Scale Data: Methods for ingesting and processing vast amounts of information.
- Preprocessing and Normalization: Ensuring data quality and consistency.
- Augmentation Strategies: Enhancing datasets to improve model performance.
Afternoon Session:
- Capstone Project
- Building a Comprehensive RAG System: Integrating all learned components into a final project.
- Optimization and Deployment: Preparing the system for real-world application.
- Review and Next Steps
- Course Recap: Summarizing key takeaways from each module.
- Further Learning Resources: Guiding continued education and exploration.
Practical Value: Fine-tuning models and efficient data handling are critical for deploying high-performance RAG systems in production environments. These skills ensure that engineers can deliver scalable and effective AI solutions.
Teaching Faculty
Asif Qamar
Chief Scientist and Educator
Background
Over more than three decades, Asif’s career has spanned two parallel tracks: as a deeply technical architect & vice president and as a passionate educator. While he primarily spends his time technically leading research and development efforts, he finds expression for his love of teaching in the courses he offers. Through this, he aims to mentor and cultivate the next generation of great AI leaders, engineers, data scientists & technical craftsmen.
Educator
He has also been an educator, teaching various subjects in AI/machine learning, computer science, and Physics for the last 32 years. He has taught at the University of California, Berkeley extension, the University of Illinois, Urbana-Champaign (UIUC), and Syracuse University. He has also given a large number of courses, seminars, and talks at technical workplaces. He has been honored with various excellence in teaching awards in universities and technical workplaces.
Chandar Lakshminarayan
Head of AI Engineering
Background
A career spanning 25+ years in fundamental and applied research, application development and maintenance, service delivery management and product development. Passionate about building products that leverage AI/ML. This has been the focus of his work for the last decade. He also has a background in computer vision for industry manufacturing, where he innovated many novel algorithms for high precision measurements of engineering components. Furthermore, he has done innovative algorithmic work in robotics, motion control and CNC.
Educator
He has also been an educator, teaching various subjects in AI/machine learning, computer science, and Physics for the last decade.
Teaching Assistants
Our teaching assistants will guide you through your labs and projects. Whenever you need help or clarification, contact them on the SupportVectors Discord server or set up a Zoom meeting.
Kate Amon
Univ. of California, Berkeley
Kayalvizhi T
Indira Gandhi National Univ
Ravi Sati
Kalasalingam Univ.
Harini Datla
Indian Statistical Institute
Kunal Lall
Univ. of Illinois, Chicago
In-Person vs Remote Participation
Plutarch
Education is not the filling of a pail, but the lighting of a fire. “For the mind does not require filling like a bottle, but rather, like wood, it only requires kindling to create in it an impulse to think independently and an ardent desire for the truth.
Our Goal: build the next generation of data scientists and AI engineers
The AI revolution is perhaps the most transformative period in our world. As data science and AI increasingly permeate the fabric of our lives, there arises a need for deeply trained scientists and engineers who can be a part of the revolution.
Over 2250+ AI engineers and data scientists trained
- Instructors with over three decades of teaching excellence and experience at leading universities.
- Deeply technical architects and AI engineers with a track record of excellence.
- More than 30 workshops and courses are offered
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- 20+ research internships finished.
Where technical excellence meets a passion for teaching
There is no dearth of technical genius in the world; likewise, many are willing and engaged in teaching. However, it is relatively rare to find someone who has years of technical excellence, proven leadership in the field, and who is also a passionate and well-loved teacher.
SupportVectors is a gathering of such technical minds whose courses are a crucible for in-depth technical mastery in this very exciting field of AI and data science.
A personalized learning experience to motivate and inspire you
Our teaching faculty will work closely with you to help you make progress through the courses. Besides the lecture sessions and lab work, we provide unlimited one-on-one sessions to the course participants, community discussion groups, a social learning environment in our state-of-the-art facility, career guidance, interview preparation, and access to our network of SupportVectors alumni.
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