Generative AI Solution Development

Introduction to RAG

• What is Retrieval-Augmented Generation (RAG)?
• Benefits of RAG in enterprise AI
• Example use cases

Preparing Data for RAG Solutions

• Data collection and preprocessing
• Document chunking and embeddings
• Building a knowledge base

Vector Search

• Concept of vector databases
• Indexing and similarity search
• Tools and frameworks (FAISS, Pinecone, Weaviate, etc.)

Assembling and Evaluating a RAG Application

• Integrating retrieval with generation
• Evaluation metrics for RAG
• Practical examples and demos

Generative AI Application Development

Foundations of Compound AI Systems

• What are compound AI systems?
• Combining multiple models and tools
• Orchestration frameworks

Building Multi-Stage Reasoning Chains

• Step-by-step reasoning pipelines
• Chaining prompts and outputs
• Error handling and fallback strategies

Agents and Cognitive Architectures

• Introduction to AI agents
• Cognitive architectures for autonomy
• Multi-agent collaboration

Generative AI Application Evaluation and Governance

Importance of Evaluating GenAI Applications

• Why evaluation matters
• Risks of unvalidated outputs

Securing and Governing GenAI Applications

• Security considerations
• Governance frameworks
• Compliance and auditability

GenAI Evaluation Techniques

• Human-in-the-loop evaluation
• Automated evaluation metrics
• Benchmarking approaches

End-to-End Application Evaluation

• Holistic evaluation strategies
• Case studies and best practices

Generative AI Application Deployment and Monitoring

Model Deployment Fundamentals

• Packaging and serving models
• Deployment environments

Batch Deployment

• Offline inference workflows
• Use cases for batch predictions

Real-Time Deployment

• APIs and microservices
• Latency considerations

AI System Monitoring

• Observability for AI systems
• Logging and alerting

LLMOps Concepts

• Continuous integration and deployment for AI
• Model versioning and rollback
• Monitoring drift and retraining