Synthetic data has rapidly evolved from a niche research concept into a core tool for modern data science and healthcare innovation. Over the past few years, I’ve worked on several projects where generating synthetic data — carefully and responsibly — made the difference between moving forward and being blocked by privacy constraints.

GRDN.AI 3.0 optimizes companion planting with an AI-infused genetic algorithm. In this article, I will break down how I developed the application, demonstrating a unique fusion of cutting-edge LLM agents with classic methodologies in decision sciences.

In this article, we will step away from RAG and walk through an example project that uses a different stack: an application powered by Metaphor API, an LLM, and prompt chaining. Empowering LLMs with tools like Metaphor API, a dynamic semantic search engine, lets us develop in a way that an LLM agent can now cruise through and extract information from the internet and social media. Not only does the LLM “connect to the internet,” but the Metaphor API allows for neural searches and is tailored for LLM use.