Implementing RAG for NLP
Duration
2 Days
Level
Intermediate Level
Design and Tailor this course
As per your team needs
Overview
This course is designed for advanced programs intended for practitioners who already have experience with NLP and Generative AI and want to design, implement, and optimize production-ready RAG pipelines. The course blends conceptual foundations with hands-on labs, enabling participants to implement RAG systems using modern ML frameworks and evaluate them against traditional fine-tuning approaches.
By the end of this course, participants will be able to:
- Understand the architecture and core components of RAG systems
- Implement Retrieval-Augmented Generation pipelines for NLP tasks
- Integrate retrieval mechanisms with large language models
- Compare RAG with traditional fine-tuning approaches
- Optimize RAG systems for performance and scalability
- Prepare RAG models for real-world deployment
Audience
- Software Developers
- Data Scientists
- ML Engineers
- AI Practitioners working with NLP and LLMs
Prerequisites
Participants should have:
- Strong working knowledge of Python
- Foundational understanding of NLP concepts
- Prior exposure to deep learning
- Familiarity with transformer models
- Experience with ML frameworks (TensorFlow / PyTorch preferred)
- Basic familiarity with Hugging Face libraries (recommended, not mandatory)
Curriculum
Environment and Tooling Recap
- Overview of TensorFlow, Keras, and Hugging Face
- Setting up the development environment using Google Colab
Introduction to Retrieval-Augmented Generation
- Limitations of standalone language models
- RAG architecture and workflow
- Key components: retriever, generator, and knowledge store
- When and why to use RAG in enterprise NLP systems
Basic RAG Implementation
- Designing a simple RAG pipeline
- Integrating retrieval with language models
- End-to-end RAG flow
Hands-On Lab
- Building a basic RAG model for an NLP task (e.g., text summarization)
- Step-by-step implementation using Hugging Face
Applying RAG to Practical NLP Use Cases
- Knowledge-based question answering
- Customizing RAG pipelines for different NLP tasks
Hands-On Lab
- Implementing a RAG-based knowledge retrieval application
RAG for Advanced NLP Challenges
- Applying RAG to conversational and interactive systems
- Improving retrieval quality and relevance
- Optimizing the retrieval component
Hands-On Lab
- Building a RAG-based conversational or chatbot application
RAG vs Fine-Tuning: Comparative Analysis
- Architectural and performance differences
- Trade-offs in training time, complexity, and cost
- Selecting the right approach for enterprise scenarios
Hands-On Lab
- Comparing RAG and fine-tuned models on performance and efficiency
Optimization and Best Practices
- Identifying models best suited for RAG
- Performance tuning and evaluation strategies
- Best practices for deploying RAG systems in production
Capstone Exercise (Mini Hackathon)
Use Case: Optimizing RAG for Real-Time Question Answering
- Improving retrieval accuracy and response quality
- Applying learned techniques to a real-world dataset
- Evaluating efficiency and model behavior
Duration
2 Days
Level
Intermediate Level
Design and Tailor this course
As per your team needs
