AWS Prompt Engineering: A Developer's Guide to CLI Usage
AWS Prompt Engineering: A Developer's Guide to CLI Usage
Overview
Prompt engineering is crucial for effectively utilizing AWS's AI and machine learning services. This guide provides working examples and practical insights for developers using AWS CLI with AI services.
Key Developer Insights
Understanding the Bedrock Runtime
When working with AWS Bedrock via CLI, there are several critical points to understand:
-
Service Namespaces:
- Use
bedrock-runtime
for model invocation - Use
bedrock
for management operations
- Use
-
Required Parameters:
- The
outfile
parameter is mandatory for invoke-model commands - Content-type must be application/json
- Binary formatting requires specific handling
- The
Working Examples
Basic Hello World Template
Here's a validated, working example for AWS Bedrock:
aws bedrock-runtime invoke-model \
--model-id anthropic.claude-v2 \
--body '{"prompt":"\n\nHuman: Say hello world\n\nAssistant:","max_tokens_to_sample":1000}' \
--content-type application/json \
--region us-east-1 \
--cli-binary-format raw-in-base64-out \
response.json && cat response.json
Key parameters to note:
max_tokens_to_sample
(notmax_tokens
)- Proper newline formatting in prompts
- Required outfile specification
Context-Aware Prompting
For more complex interactions, include proper context:
aws bedrock-runtime invoke-model \
--model-id anthropic.claude-v2 \
--body '{
"prompt": "\n\nHuman: You are a cloud architecture expert. Suggest three best practices for AWS Lambda functions.\n\nAssistant:",
"max_tokens_to_sample": 1000
}' \
--content-type application/json \
--region us-east-1 \
--cli-binary-format raw-in-base64-out \
response.json
Common Pitfalls and Solutions
-
Output Handling:
- Always specify an outfile
- Use
cat
to view results - Consider using
--query
for JSON parsing
-
Model Parameters:
- Use correct parameter names (
max_tokens_to_sample
vsmax_tokens
) - Include proper prompt formatting with newlines
- Remember content-type specifications
- Use correct parameter names (
-
Binary Handling:
- Use
--cli-binary-format raw-in-base64-out
- Handle response streaming appropriately
- Consider file output for large responses
- Use
Best Practices for Production Use
Security and Authentication
- Use IAM roles with least privilege
- Keep credentials secure
- Implement proper error handling
Performance Optimization
- Cache responses when appropriate
- Implement retry mechanisms
- Monitor token usage
Error Handling
- Implement proper try-catch mechanisms
- Log errors comprehensively
- Have fallback options
Service-Specific Examples
Amazon Comprehend
For sentiment analysis:
aws comprehend detect-sentiment \
--language-code en \
--text "This product exceeded my expectations" \
--region us-east-1
Amazon Translate
For translation tasks:
aws translate translate-text \
--source-language-code en \
--target-language-code es \
--text "Hello, how are you?" \
--region us-east-1
Development Workflow Tips
-
Testing Strategy:
- Start with simple prompts
- Gradually increase complexity
- Test edge cases thoroughly
-
Version Control:
- Keep track of successful prompts
- Document parameter changes
- Maintain prompt templates
-
Monitoring and Logging:
- Track token usage
- Monitor response times
- Log prompt-response pairs
Conclusion
Effective CLI usage with AWS AI services requires attention to detail and understanding of service-specific requirements. Keep these key points in mind:
- Always use the correct service namespace (
bedrock-runtime
vsbedrock
) - Pay attention to parameter names and formatting
- Handle binary responses appropriately
- Implement proper error handling and monitoring
- Test thoroughly in non-production environments
Remember to regularly consult AWS documentation as services and capabilities evolve.