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In Prompts We Trust - Jiaranai Keatnuxsuo - NDC Sydney 2024
Improve trust in AI models by crafting effective prompts, understanding language nuances, and deploying models responsibly, with expert insights on GPT-3 and human-AI collaboration.
- Trust in prompts can be improved through proper contextualization, nuance, and understanding of language.
- Prompts should be concise, clear, and focused, with a maximum of 5-3 sentences.
- TopK and TopP techniques can help with ranking and filtering responses, but may not work well for complex tasks.
- Chain of thought prompting can be effective for complex tasks, but may require multiple iterations.
- Contextualization is crucial for large language models to provide accurate and relevant responses.
- The author recommends using open-source models like GPT-3 for more cost-effective and accessible AI solutions.
- Deployments of AI models should consider the nuances of human behavior, including emotions and biases.
- Trust in AI models can be established through transparency, explainability, and minimization of risk.
- Effective prompts can help bridge the gap between human language and AI understanding.
- Multi-step problem-solving can be achieved through iterative processes of problem engineering.
- AI can be used for tasks that require creativity, judgment, and decision-making, such as generating poetry and writing.