Prompting Tips for Large Language Models with Vision Capabilities

Large Language Models (LLMs) like GPT-4o, Google Gemini, and Anthropic Claude have advanced into multimodal systems, enabling them to process various inputs such as images and videos alongside text, which equips them to handle diverse computer vision tasks. The blog provides guidance on effectively creating prompts for these models to solve computer vision challenges, particularly using Google Gemini, a suite of models by Google DeepMind designed for multimodality. Google Gemini, accessible via an API key, is integrated into platforms like Roboflow Workflows, allowing users to construct AI-powered workflows without code. It emphasizes strategies for effective prompting and leveraging techniques like system instructions, few-shot examples, and structured output generations to enhance accuracy and reduce errors in outputs. Furthermore, it discusses using grounding search to link model insights with real-world knowledge, reducing hallucinations and improving reliability. The blog showcases the application of these techniques in tasks like object detection and OCR, underscoring the role of parameters like temperature and thinking budgets in refining model responses.