Automated prompt optimization for generative language models

Abstract

Techniques for generating optimized input prompts for generative language models combines automated prompt refinement with intelligent context retrieval. A prompt optimization service automatically refines initial prompt instructions through controlled mutation and evaluation cycles using multiple generative language models. A context management service maintains a vector database of historical examples pre-encoded as embedding vectors, enabling efficient retrieval of semantically similar examples for new tasks. A dynamic prompt generator assembles optimized prompts by combining refined instructions with relevant historical examples and task context. The system supports deployment with both large language models and smaller fine-tuned models, enabling efficient operation across diverse applications. Configurable parameters allow balancing optimization quality against computational costs. The system maintains efficiency through single-call mutations, fast vector similarity search, and support for compact fine-tuned models that reduce resource requirements while preserving response quality.