Fine-Tuning for Retrieval-Augmented Generation (RAG) Systems Training Course
Fine-tuning 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 NLP engineers and knowledge management teams aiming to fine-tune RAG pipelines to improve performance in question answering, enterprise search, and summarization tasks.
Upon completion of this training, participants will be able to:
- Grasp the architecture and workflow of RAG systems.
- Fine-tune retriever and generator components for domain-specific data.
- Assess RAG performance and implement enhancements using PEFT techniques.
- Deploy optimized RAG systems for internal or production environments.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Customization Options
- To request a customized version of this course, please contact us to arrange.
Course Outline
Introduction to Retrieval-Augmented Generation (RAG)
- Understanding RAG and its significance for enterprise AI
- Key components of a RAG system: retriever, generator, and document store
- Comparison with standalone LLMs and vector search solutions
Setting Up a RAG Pipeline
- Installing and configuring Haystack or comparable frameworks
- Document ingestion and preprocessing techniques
- Connecting retrievers to vector databases (e.g., FAISS, Pinecone)
Fine-Tuning the Retriever
- Training dense retrievers using domain-specific data
- Utilizing sentence transformers and contrastive learning
- Evaluating retriever quality via top-k accuracy
Fine-Tuning the Generator
- Selecting base models (e.g., BART, T5, FLAN-T5)
- Distinguishing between instruction tuning and supervised fine-tuning
- Applying LoRA and PEFT methods for efficient updates
Evaluation and Optimization
- Metrics for assessing RAG performance (e.g., BLEU, EM, F1)
- Addressing latency, retrieval quality, and hallucination reduction
- Experiment tracking and iterative improvement strategies
Deployment and Real-World Integration
- Deploying RAG within internal search engines and chatbots
- Considering security, data access, and governance aspects
- Integrating with APIs, dashboards, or knowledge portals
Case Studies and Best Practices
- Enterprise use cases in finance, healthcare, and legal sectors
- Managing domain drift and knowledge base updates
- Future trends in retrieval-augmented LLM systems
Summary and Next Steps
Requirements
- A solid understanding of natural language processing (NLP) concepts
- Experience with transformer-based language models
- Familiarity with Python and fundamental machine learning workflows
Target Audience
- NLP engineers
- Knowledge management teams
Open Training Courses require 5+ participants.
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