In this post, we explore how Crypto.com used user and system feedback to continuously improve and optimize our instruction prompts. This feedback-driven approach has enabled us to create more effective prompts that adapt to various subsystems while maintaining high performance across different use cases.
Optimizing enterprise AI assistants: How Crypto.com uses LLM reasoning and feedback for enhanced efficiency
