Course Code: ftrag
Duration: 14 hours
Prerequisites:
  • 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
Overview:

Fine-Tuning for Retrieval-Augmented Generation (RAG) Systems is the process of optimizing how large language models retrieve and generate relevant information from external sources for enterprise applications.

This instructor-led, live training (online or onsite) is aimed at 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

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