This site is designed to help you understand and effectively use Generative AI models for research. We cover three key areas of Generative AI-driven research support:
- Generative AI for Scientific Discovery – An overview of how cutting-edge models like GANs, diffusion models, and graph networks are driving innovation in fields like biology, physics, and materials science.
- Deep Research Tools – Explore advanced generative AI models like ChatGPT, Perplexity, and Consensus that combine real-time search with citation tracking, enhancing accuracy and transparency in research.
- General Research Methods – Discover generative AI tools for coding, visualization, and creative tasks that can improve research productivity and efficiency.
Generative AI is not here to replace researchers – it’s here to empower them. The tools and methods outlined here are designed to complement your expertise, helping you uncover insights, accelerate discovery, and elevate the quality of your work. Dive into the resources, explore the guides, and start integrating generative AI into your research workflow today.
Guiding Principles
While Generative AI tools like ChatGPT, Claude, and others can enhance research efficiency and creativity, they also come with important risks and limitations. Understanding these challenges and adopting thoughtful best practices can help researchers use generative AI effectively while safeguarding research integrity and accuracy. When using Generative AI for research, it is important to recognize that these models are trained on large datasets that may reflect underlying biases, inaccuracies, and inconsistencies. AI-generated content is not inherently reliable, it reflects patterns in the training data rather than verified knowledge. As such, researchers should approach AI-generated outputs with a critical mindset, carefully evaluating the quality, accuracy, and appropriateness of the information produced.
Research Tools
This page contains a list of tools including models for literature search, summary, and idea generation and for data analysis and visualization that researchers can utilize during their research.
User Guide
This user guide contains some common questions researchers have when wanting to use Generative AI in their research.
Guided Examples
Below are some examples of how to incorporate Generative AI in you research.
Using GenAI in Coding
This quick-start guide researchers with little programming experience start coding with the help of GenAI.
Starting Data Visualization and Analysis with ChatGPT
Explore how to use Chat GPT4’s “data analysis” feature effectively. This guide covers code organization, error checking, data visualization, and translation between coding languages.
Setting up Custom GPT in Chat GPT 4
This guide shows how to set up custom GPT within Chat GPT 4, which is especially useful if you would like Chat GPT to carry out a specific task repeatedly, or prefer a specific style of output.
Effective Prompting
This guide demonstrates a number of strategies to craft your prompts in order to shape the content and style of GenAI outputs.