Best Practices

Learn how to optimize your CognitiveX implementation for cost, performance, security, and reliability.

Cost Optimization

Reduce costs through caching, batching, and smart routing

  • •Enable caching for frequently asked questions
  • •Use batch operations for bulk data
  • •Choose appropriate LLM models for tasks
  • •Monitor and set usage limits

Performance

Optimize latency and throughput for production workloads

  • •Use Redis caching for hot data
  • •Implement request batching
  • •Optimize embedding generation
  • •Use connection pooling

Security

Secure your implementation with best practices

  • •Rotate API keys regularly
  • •Use environment variables
  • •Implement rate limiting
  • •Enable audit logging

Testing

Test your AI workflows effectively

  • •Write unit tests for logic
  • •Use mock data for LLM calls
  • •Test edge cases thoroughly
  • •Monitor quality metrics