LLM Foundations: Building Effective Applications for Enterprises

Explore design considerations and best practices for building generative AI-powered applications at enterprise scale.

Introduction

• Starting your GenAI adoption journey
• Course content and prerequisites

1. Generative AI in Enterprises

• The GenAI revolution
• How GenAI is impacting enterprises
• GenAI challenges for enterprises
• GenAI adoption process

2. GenAI Use Case Selection

• Popular GenAI use cases
• Identifying and evaluating GenAI use cases
• Creating a GenAI roadmap
• Building proof-of-concepts for GenAI
• Course use case: GenAI-powered email helpdesk

3. Choosing GenAI Models

• The GenAI model landscape
• Selection criteria for GenAI models
• Build vs. buy for GenAI
• GenAI model deployment considerations
• Impact of training data in GenAI

4. Evaluation and Fine-Tuning for GenAI

• Evaluating GenAI models
• Fine-tuning GenAI models
• Building datasets for evaluation and fine-tuning
• Metrics for GenAI
• Course use case: Evaluation and model selection

5. Design Considerations for GenAI Applications

• A typical GenAI app
• Batch generation with GenAI models
• User and API interfaces
• Prompt engineering
• Data collection and monitoring
• Course use case: Architecture

6. Safety and Trust with GenAI

• Ethical and legal considerations
• Protecting against vulnerabilities
• Toxicity and bias in GenAI
• Hallucinations
• Course use case: Guardrails

7. Managing GenAI Application Performance

• Performance goals for GenAI apps
• Improving GenAI accuracy
• Reducing latency for GenAI
• GenAI cost control
• Course use case: Performance considerations

Conclusion

• Continue your GenAI journey