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 pertinent 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 tailored to 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 application.
- Hands-on implementation within a live lab environment.
Customization Options
- To request customized training for this course, please contact us to make arrangements.
Course Outline
Introduction to Retrieval-Augmented Generation (RAG)
- Understanding RAG and its significance for enterprise AI.
- RAG system components: retriever, generator, and document store.
- Comparison with standalone LLMs and vector search.
Setting Up a RAG Pipeline
- Installing and configuring Haystack or comparable 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.
- 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).
- Differentiating between instruction tuning and supervised fine-tuning.
- Employing LoRA and PEFT methods for efficient updates.
Evaluation and Optimization
- Metrics for assessing RAG performance (e.g., BLEU, EM, F1).
- Managing latency, retrieval quality, and reducing hallucinations.
- Experiment tracking and iterative improvement strategies.
Deployment and Real-World Integration
- Deploying RAG in internal search engines and chatbots.
- Addressing security, data access, and governance considerations.
- Integration with APIs, dashboards, or knowledge portals.
Case Studies and Best Practices
- Enterprise use cases within 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.
Audience
- NLP engineers.
- Knowledge management teams.
Open Training Courses require 5+ participants.
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