AWS Bedrock Best Practices Advanced

This final lesson provides comprehensive best practices for building production-grade applications with AWS Bedrock. These practices cover security, cost management, monitoring, prompt engineering, and production deployment patterns.

Production Checklist

Checklist
SECURITY:
  [ ] IAM roles scoped to specific models (bedrock:InvokeModel)
  [ ] VPC endpoint for private Bedrock access
  [ ] CloudTrail logging for all API calls
  [ ] Guardrails configured for content safety
  [ ] Input validation before sending to model
  [ ] Output sanitization before returning to users

COST MANAGEMENT:
  [ ] Token budgets per application/user
  [ ] CloudWatch metrics for token usage tracking
  [ ] Smaller models for simple tasks (Haiku vs Sonnet)
  [ ] Caching for repeated/similar queries
  [ ] Batch API for offline processing (50% savings)

RELIABILITY:
  [ ] Retry logic with exponential backoff
  [ ] Circuit breaker for model endpoint failures
  [ ] Fallback to alternative model on errors
  [ ] Request timeout configuration
  [ ] Rate limiting in API Gateway

MONITORING:
  [ ] CloudWatch dashboards for latency, throughput, errors
  [ ] Custom metrics for response quality
  [ ] Logging prompts and responses (with PII redaction)
  [ ] Alert on token budget overruns
  [ ] Track model invocation costs by application

Cost Optimization Strategies

StrategySavingsImplementation
Model tiering50-80%Use Haiku for simple tasks, Sonnet for complex ones
Prompt cachingUp to 90%Cache system prompts and repeated context
Batch inference50%Use batch API for offline processing
Response cachingVariableCache identical queries with ElastiCache
Token optimization20-40%Concise prompts, lower max_tokens where possible

Prompt Engineering Tips for Bedrock

  • Use system prompts - Set role and constraints in the system message for consistent behavior
  • Be specific about format - Tell the model exactly how to structure its output (JSON, lists, tables)
  • Include examples - Few-shot prompting dramatically improves output quality for specific tasks
  • Set guardrails in prompts - Layer prompt-level instructions with Bedrock Guardrails for defense in depth
  • Version your prompts - Store prompts in S3 or Parameter Store for versioning and A/B testing
Course Complete: You now have comprehensive knowledge of building generative AI applications with AWS Bedrock. From foundation model selection to agents and knowledge bases, you are equipped to build production-ready AI applications on AWS.

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