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) is designed for intermediate-level NLP engineers and knowledge management teams seeking to fine-tune RAG pipelines to improve performance in question answering, enterprise search, and summarization scenarios.
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.
- Evaluate 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 practical sessions.
- Hands-on implementation within a live-lab environment.
Course Customization Options
- To request customized training for this course, please contact us to arrange it.
Course Outline
Introduction to Retrieval-Augmented Generation (RAG)
- Understanding RAG and its significance for enterprise AI.
- Components of a RAG system: retriever, generator, and 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.
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