Generate a good-fit Generative AI output with Genetic Algorithm

In today’s world where Generative AI is revolutionizing the way business works, the contextualization and accuracy of the generated content is becoming increasingly critical. With this technology generating unique and new content every time, making this output more relevant and contextual to my business, customers and organizations is of prime importance and is a challenge today. This paper talks about an approach where Genetic Algorithm can augment the Generative AI output to learn and evolve from every generated output and automatically refine the prompts and embeddings to generate more accurate outputs. We used the evolutionary algorithms to check the fitness score with every new output generated and tweak mutation and crossover operators to evolve to generated outputs that are closer to the organization’s business context. This can then feed into the enterprise knowledge corpus or vector store that can then be embedded into the prompts to generate a good fit response.