RAG and Enterprise Knowledge Assistants Training in Madrid, Spain

This training provides a comprehensive deep dive into Retrieval Augmented Generation (RAG) and its application in building enterprise knowledge assistants. Participants will learn to architect, implement, and optimize RAG pipelines that retrieve relevant information from enterprise knowledge bases and generate accurate, context-aware responses. The course covers foundational concepts, advanced retrieval techniques, integration with large language models, and deployment strategies for production environments. Through hands-on exercises and real-world use cases, learners will gain the skills to create scalable, secure, and efficient knowledge assistants that enhance organizational productivity and decision-making.

Foundations of RAG and Enterprise Knowledge Assistants

• Overview of RAG architecture and its role in enterprise AI
• Comparing RAG with traditional search and fine-tuning
• Key components: retrieval, augmentation, generation
• Use cases: customer support, internal knowledge bases, compliance

Document Ingestion and Chunking Strategies

• Preprocessing enterprise documents (PDFs, wikis, databases)
• Chunking techniques: fixed-size, semantic, recursive
• Metadata extraction and indexing
• Handling multi-format and multilingual content

Embeddings and Vector Databases

• Understanding text embeddings and similarity metrics
• Vector database options: open-source and managed
• Indexing, querying, and hybrid search
• Optimizing for low latency and high recall

Retrieval Optimization and Re-ranking

• Advanced retrieval: hybrid search, query expansion, fusion
• Re-ranking models for precision improvement
• Filtering by metadata, date, and access control
• Handling ambiguous or multi-turn queries

Augmentation and Prompt Engineering

• Designing effective prompts for RAG systems
• Context window management and token optimization
• Incorporating retrieved chunks with instruction templates
• Handling hallucinations and irrelevant retrieval

Integration with Large Language Models

• Selecting and hosting LLMs for enterprise RAG
• API integration and response streaming
• Fine-tuning vs. prompt-based customization
• Ensuring consistency and safety in outputs

Deployment, Monitoring, and Security

• Architecting scalable RAG systems for production
• Monitoring retrieval and generation quality
• Access control, data privacy, and compliance
• Feedback loops and continuous improvement