Harnessing EDA & Workflows to Build Real World GenAI Apps • Uma Ramadoss & Veda Raman • GOTO 2024

Event-driven architectures and workflows (like AWS Step Functions) provide better observability, extensibility and control when building GenAI applications

Breaking down large prompts into smaller tasks (prompt decomposition) allows for:

• Parallel processing
• Using smaller/specialized LLMs per task
• Better cost efficiency
• Improved accuracy
• Easier maintenance

Human-in-the-loop validation is crucial for content moderation and quality control, even though it increases latency

Using managed services like Amazon Bedrock provides:

• Access to multiple foundation models through single API
• Security and data privacy
• Scalability without infrastructure management
• Pay-per-use pricing model

The Strangler Fig pattern helps gradually introduce GenAI capabilities into existing applications without full re-architecture

Event routers (like Amazon EventBridge) help decouple services and allow selective event consumption

Prompt engineering techniques like persona patterns and prompt chaining improve response quality

As GenAI applications move from POC to production:

• Prompts tend to grow larger
• Requirements expand
• Testing complexity increases
• Integration needs multiply

Workflow orchestration provides:

• Error handling and retries
• Process visibility
• Task coordination
• Integration capabilities

Building evolutionary architecture with event-driven patterns enables faster development and easier model switching as technology advances