LlamaIndex Training for RAG Applications

Build Intelligent Document Processing Systems at Scale

Master Retrieval-Augmented Generation in 3 Days

Transform unstructured data into intelligent, queryable knowledge bases. Learn to build production RAG systems that deliver accurate, contextual responses from your organization’s documents.

🎯 Course Overview

This comprehensive 3-day course teaches you to build sophisticated RAG (Retrieval-Augmented Generation) applications using LlamaIndex. You’ll master document processing, vector storage, and advanced retrieval techniques used in production systems.

What You’ll Master

• 📚 Document Processing: Handle PDFs, docs, web content, and more
• 🔍 Advanced Retrieval: Multi-modal search and hybrid strategies
• 🗄️ Vector Databases: Integration with Pinecone, Weaviate, ChromaDB
• 🎯 Accuracy Optimization: Improve relevance and reduce hallucinations
• 🚀 Production Deployment: Scale to millions of documents

Who Should Attend

• Engineers building knowledge management systems
• Data scientists working with unstructured data
• Architects designing AI-powered search
• Product teams creating intelligent applications
• Anyone building RAG or document Q&A systems

📚 Detailed Curriculum

Day 1: Foundations & Document Processing

Morning Session: LlamaIndex Fundamentals

• RAG Architecture Overview

  • When and why to use RAG
  • LlamaIndex vs. alternatives
  • Core components and concepts
  • Production considerations

• Document Loading & Parsing

  • Built-in data connectors
  • Custom loader development
  • Handling complex formats
  • Metadata extraction

• Hands-On Lab 1: Multi-Format Document Pipeline

  • Load PDFs, Word docs, and web pages
  • Extract and preserve metadata
  • Handle tables and images
  • Build unified document store

Afternoon Session: Indexing Strategies

• Index Types & Selection

  • Vector Store Index
  • List Index variations
  • Tree Index structures
  • Keyword Table Index
  • Composable Graph Index

• Chunking & Text Processing

  • Optimal chunk sizes
  • Overlap strategies
  • Semantic chunking
  • Hierarchical chunking

• Hands-On Lab 2: Build Multiple Index Types

  • Create indexes for different use cases
  • Compare performance metrics
  • Implement hybrid approaches
  • Optimize for your data

Day 2: Advanced Retrieval & Optimization

Morning Session: Query Engineering

• Query Transformation

  • Query decomposition
  • Sub-question generation
  • Hypothetical document embeddings
  • Query routing strategies

• Retrieval Strategies

  • Similarity search optimization
  • Hybrid search (keyword + vector)
  • Reranking techniques
  • Contextual compression

• Hands-On Lab 3: Advanced Query Pipeline

  • Implement query transformation
  • Build custom retrievers
  • Add reranking layers
  • Measure improvement metrics

Afternoon Session: Response Synthesis

• Response Generation

  • Synthesis modes (refine, tree, simple)
  • Streaming responses
  • Citation management
  • Answer validation

• Context Management

  • Context window optimization
  • Relevant context selection
  • Token budget management
  • Multi-document synthesis

• Hands-On Lab 4: Production Response Pipeline

  • Build streaming RAG system
  • Add source citations
  • Implement fact checking
  • Handle complex queries

Day 3: Production Systems & Advanced Features

Morning Session: Vector Database Integration

• Vector Store Deep Dive

  • Pinecone optimization
  • Weaviate configuration
  • ChromaDB deployment
  • Qdrant best practices

• Performance Tuning

  • Embedding model selection
  • Batch processing strategies
  • Caching mechanisms
  • Horizontal scaling

• Hands-On Lab 5: Production Vector Store

  • Deploy to cloud vector DB
  • Implement backup strategies
  • Set up monitoring
  • Optimize query performance

Afternoon Session: Advanced Applications

• Multi-Modal RAG

  • Image and text retrieval
  • Table understanding
  • Chart interpretation
  • Cross-modal search

• Agent Integration

  • LlamaIndex agents
  • Tool use in RAG
  • Dynamic retrieval
  • Conversational memory

• Hands-On Lab 6: Complete RAG Application

  • Build multi-modal search
  • Add conversational interface
  • Implement access controls
  • Deploy to production

🛠️ Real-World Projects

Project 1: Enterprise Knowledge Base

Build a comprehensive knowledge management system that:

• Ingests documents from multiple sources
• Provides accurate Q&A with citations
• Handles access control and permissions
• Scales to millions of documents

Project 2: Technical Documentation Assistant

Create an intelligent documentation system that:

• Understands code and technical content
• Provides contextual help
• Suggests related information
• Maintains version awareness

Project 3: Research Paper Analyzer

Develop a research assistant that:

• Processes academic papers
• Extracts key findings
• Identifies connections between papers
• Generates literature reviews

💡 Advanced Topics Covered

Evaluation & Testing

• RAG evaluation metrics
• Test dataset creation
• A/B testing strategies
• Continuous improvement loops

Security & Privacy

• Data isolation strategies
• PII detection and handling
• Access control implementation
• Audit logging

Cost Optimization

• Embedding cost reduction
• Efficient retrieval strategies
• Caching architectures
• Resource allocation

Integration Patterns

• API design for RAG
• Webhook integrations
• Event-driven architectures
• Microservices patterns

📋 Prerequisites

Required Knowledge

• Python programming (intermediate)
• Basic understanding of APIs
• Familiarity with databases
• Command line proficiency

Recommended Background

• Experience with search systems (helpful)
• Basic ML concepts (beneficial)
• Document processing experience (useful)

Technical Requirements

• Laptop with 16GB+ RAM
• Python 3.8+ environment
• Docker installed
• Cloud account (for vector DB labs)

💰 Pricing & Options

Training Formats

On-Site Training

• Price: $15,000 for up to 12 participants
• Duration: 3 consecutive days
• Includes: Customization for your use cases
• Bonus: Architecture review session

Virtual Training

• Price: $10,000 for up to 12 participants
• Duration: 3 days (6 hours per day)
• Format: Interactive online sessions
• Support: 30-day post-training access

Public Classes

• Price: $1,995 per participant
• Schedule: Monthly offerings
• Locations: Major tech hubs
• Next Date: View Calendar

What’s Included

• Complete course materials
• Production-ready code templates
• Vector DB credits for labs
• LlamaIndex Pro features (3 months)
• Certificate of completion
• Alumni community access

🎯 Learning Outcomes

Upon completion, you will be able to:

✅ Design and build production RAG systems
✅ Process complex document types efficiently
✅ Optimize retrieval accuracy and speed
✅ Integrate multiple vector databases
✅ Handle multi-modal content
✅ Deploy scalable document pipelines
✅ Implement proper evaluation metrics
✅ Build cost-effective solutions

👨‍🏫 Expert Instructors

Learn from engineers who’ve built RAG systems processing millions of documents:

• Production experience: Currently building enterprise RAG
• Open source contributors: Active in LlamaIndex community
• Real implementations: Deployed systems you can reference
• Continuous updates: Course evolves with the framework

🚀 Get Started

Build Your Next-Gen Knowledge System

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📚 Resources & Materials

Pre-Course Resources

• RAG Fundamentals Guide
• Vector Database Comparison
• LlamaIndex Quick Start

Post-Course Support

• 30-day instructor access
• Private Slack channel
• Monthly office hours
• Update notifications

Related Training

• Vector Database Mastery
• OpenAI + RAG Integration
• Production ML Systems

❓ Frequently Asked Questions

Q: How is this different from LangChain training?
A: LlamaIndex focuses specifically on RAG and document processing, while LangChain covers broader agent/chain patterns. We offer both.

Q: Can we bring our own documents?
A: Yes! We encourage it. We’ll help you build prototypes with your actual data.

Q: What vector databases do you cover?
A: All major ones: Pinecone, Weaviate, ChromaDB, Qdrant, plus PostgreSQL with pgvector.

Q: Do you cover multimodal RAG?
A: Yes, Day 3 includes image, table, and chart understanding with RAG.

Q: How large can the document sets be?
A: We’ll work with sets from thousands to millions of documents, covering various scale challenges.

View All FAQs →

🏆 Success Stories

🎓 Certification

Earn your certification by completing:

• All hands-on labs
• Final project presentation
• Knowledge assessment
• Peer review exercise

Certified graduates receive:

• Digital certificate and badge
• LinkedIn verification
• Portfolio project listing
• Recruiter visibility (optional)