In a paper published in April this year, researchers showed how speaking more collaboratively to LLMs seems to generate better responses. The paper describes how Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, and Xinyun Chen conducted a series of experiments to explore how different strategies for prompts delivered different answers.
In this post, I will highlight two approaches to getting to a better answer from ChatGPT. The first is Refining and the second is Regenerating.
In this tip I show how ChatGPT can convert the text in an image into text you an work with.
An international group has just published a new report on the safety of AI. You can download it from here. The report comprises more than 75,000 words, so I decided to use ChatGPT 4o to speed up my review of the documents. Below, I set out my process and what I found out about the report.
One of the surprises about the sudden and widespread interest in Synthetic Data is that it has forced us to revisit things we have been doing for decades. This revisiting of old practices is useful for framing where we think Synthetic Data should go and how it should be evaluated and regulated. In this post I explore why weighting is a form of synthetic data and what that means for researchers.
AI is a hot topic, and in the insight and research ecosystem, the hottest topic is Synthetic Data. In this post, I look at what Synthetic Data means, why it is such a hot topic, some of the key issues, and share some thoughts about Synthetic Data and its use.
In this post, I want to highlight one way that ChatGPT can help you be more efficient. In this example, I have taken a Focus Group Transcript from a published study and used ChatGPT to help speed up my analysis.
Meme’s are a powerful medium of communication, the pass from person to person because they catch the attention, are memorable, and are easy to share. In this post Ray Poynter shows how to use them to help communicate the story in the data.
NewMR has recently conducted its third wave of a study on the State of Insights, focusing on Optimism/Pessimism, the main Positives and Negatives. Here is a summary of the finding and a link to the full report.
The third wave of the NewMR study looking at the state of insights has just closed its data collection and we have the views of 330 insight and research professionals from around the world. A few days ago, I shared a word cloud based on one part of the data. As the next step in looking at the data, I used ResearchWiseAI to produce an automated summary. This was achieved by loading a raw export of the 330 responses into the platform and I asked the following question “What is the state of optimism/pessimism in the research and insights world? What are the main things that researchers are positive about and worried about?”
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