Working with Large Language Models (LLMs) like Google Gemini often presents a significant challenge: how do you effectively handle large context data without hitting token limits or incurring excessive costs? This article dives deep into a practical PHP implementation, the Gemini_Handler class, that demonstrates advanced strategies for managing extensive inputs and orchestrating multi-turn, agentic workflows with Gemini. Whether you're generating complex code, detailed reports, or intricate UI designs, understanding how to feed large datasets and refine LLM outputs iteratively is crucial for robust AI applications. We'll break down the techniques used in this class, making them accessible even for beginners looking to level up their LLM integration skills. The Gemini_Handler Class: An Overview for Gemini Large Context Handling The Gemini_Handler class is designed to streamline interactions with the Google Gemini API.…