Fine-Tuning for Retrieval-Augmented Generation (RAG) Systems Training Course
Fine-Tuning for Retrieval-Augmented Generation (RAG) Systems involves optimizing how large language models retrieve and generate relevant information from external sources for enterprise applications.
This instructor-led, live training (available online or onsite) targets intermediate-level NLP engineers and knowledge management teams who wish to fine-tune RAG pipelines to enhance performance in question answering, enterprise search, and summarization use cases.
By the end of this training, participants will be able to:
- Understand the architecture and workflow of RAG systems.
- Fine-tune retriever and generator components for domain-specific data.
- Evaluate RAG performance and apply improvements through PEFT techniques.
- Deploy optimized RAG systems for internal or production use.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to Retrieval-Augmented Generation (RAG)
- What is RAG and why it matters for enterprise AI
- Components of a RAG system: retriever, generator, document store
- Comparison with standalone LLMs and vector search
Setting Up a RAG Pipeline
- Installing and configuring Haystack or similar frameworks
- Document ingestion and preprocessing
- Connecting retrievers to vector databases (e.g., FAISS, Pinecone)
Fine-Tuning the Retriever
- Training dense retrievers using domain-specific data
- Using sentence transformers and contrastive learning
- Evaluating retriever quality with top-k accuracy
Fine-Tuning the Generator
- Selecting base models (e.g., BART, T5, FLAN-T5)
- Instruction tuning vs. supervised fine-tuning
- LoRA and PEFT methods for efficient updates
Evaluation and Optimization
- Metrics for evaluating RAG performance (e.g., BLEU, EM, F1)
- Latency, retrieval quality, and hallucination reduction
- Experiment tracking and iterative improvement
Deployment and Real-World Integration
- Deploying RAG in internal search engines and chatbots
- Security, data access, and governance considerations
- Integration with APIs, dashboards, or knowledge portals
Case Studies and Best Practices
- Enterprise use cases in finance, healthcare, and legal
- Managing domain drift and knowledge base updates
- Future directions in retrieval-augmented LLM systems
Summary and Next Steps
Requirements
- An understanding of natural language processing (NLP) concepts
- Experience with transformer-based language models
- Familiarity with Python and basic machine learning workflows
Audience
- NLP engineers
- Knowledge management teams
Open Training Courses require 5+ participants.
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