Develop technical POCs to validate feasibility, performance, and business value

A proof of concept validates whether the proposed GenAI solution works before committing to full production implementation.

GenAI behavior is difficult to predict from documentation alone.

A POC can test:

• response quality
• latency
• token usage
• model capability
• retrieval quality
• integration feasibility
• cost
• business usefulness

A practical POC lifecycle:

Define success criteria
        ↓
Select representative data
        ↓
Select candidate FM(s)
        ↓
Build minimum architecture
        ↓
Run representative workloads
        ↓
Measure quality / latency / cost
        ↓
Compare alternatives
        ↓
Production decision

• Amazon Bedrock
• Amazon S3
• AWS Lambda
• Amazon CloudWatch
• Amazon API Gateway
• Amazon SageMaker AI

Define measurable criteria before testing:

Use POCs when:

• multiple models are candidates
• requirements are uncertain
• a new architecture pattern is being evaluated
• business value has not yet been proven

• Testing only easy examples
• Using unrealistic data
• Measuring only model quality
• Ignoring operational cost
• Building a full production system before validating assumptions

A POC should reduce uncertainty, not become a premature production implementation.

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