Fine-Tuning for Retrieval-Augmented Generation (RAG) Systems Training Course
Optimizing how large language models extract and produce relevant insights from external sources for enterprise applications is the core of Fine-Tuning for Retrieval-Augmented Generation (RAG) Systems.
Designed for intermediate-level NLP engineers and knowledge management teams, this instructor-led live training (available online or onsite) focuses on refining RAG pipelines to boost performance in question answering, enterprise search, and summarization tasks.
Upon completion, participants will be equipped to:
- Comprehend the architecture and operational workflows of RAG systems.
- Adapt retriever and generator components for domain-specific datasets.
- Assess RAG performance and implement enhancements using PEFT techniques.
- Roll out optimized RAG systems for internal operations or production environments.
Course Format
- Interactive lectures and facilitated discussions.
- Extensive exercises and practical drills.
- Live-lab implementation for hands-on experience.
Customization Options
- For tailored training requirements, please reach out to us to coordinate details.
Course Outline
Introduction to Retrieval-Augmented Generation (RAG)
- Defining RAG and its significance in enterprise AI
- Key components: retriever, generator, and document store
- Differentiating RAG from standalone LLMs and vector search
Establishing a RAG Pipeline
- Installing and configuring frameworks such as Haystack
- Document ingestion and preprocessing strategies
- Linking retrievers to vector databases (e.g., FAISS, Pinecone)
Retriever Fine-Tuning
- Training dense retrievers with domain-specific data
- Leveraging sentence transformers and contrastive learning
- Measuring retriever quality via top-k accuracy
Generator Fine-Tuning
- Selecting foundational models (e.g., BART, T5, FLAN-T5)
- Instruction tuning compared to supervised fine-tuning
- Efficient updates using LoRA and PEFT methods
Evaluation and Optimization
- Performance metrics for RAG (e.g., BLEU, EM, F1)
- Addressing latency, retrieval accuracy, and hallucinations
- Tracking experiments and driving iterative improvements
Deployment and Integration
- Implementing RAG in internal search engines and chatbots
- Considerations for security, data access, and governance
- Integrating with APIs, dashboards, or knowledge portals
Case Studies and Best Practices
- Enterprise applications in finance, healthcare, and legal sectors
- Mitigating domain drift and updating knowledge bases
- Emerging trends in retrieval-augmented LLM systems
Summary and Next Steps
Requirements
- Fundamental knowledge of natural language processing (NLP) principles
- Practical experience with transformer-based language models
- Proficiency in Python and basic machine learning workflows
Intended Audience
- NLP engineers
- Knowledge management teams
Open Training Courses require 5+ participants.
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