Skip to main content

Using ChatGPT+ Data analyst to create tweets

The power of artificial intelligence (AI) to transform our approach to data analysis is becoming clearer. My recent experience with a tool called Data Analysis part of the ChatGPT Plus paid version, gives an example of transformative power. You don't need the paid version of ChatGPT to do this but you would need to copy the text in the prompt in the paid version you can load files of content.

After a conference presentation, I was part of, I was interested in seeing how these tools extract and summarise key points from a PowerPoint file. This is where the Data Analysis Analyst tool came into play. With its ability to upload and process files, I was able to convert the presentation into plain text, which the tool then read and summarised with reasonable accuracy. The summary it provided was concise, capturing the essence of the presentation effectively.

But the capabilities of the Data Analysis Analyst tool extend far beyond text extraction and summarisation. One of the most intriguing features I explored was its ability to generate insights from the summarized data. By simply instructing the tool to "find four insights and produce four Twitter posts from the insight," I was able to obtain potential tweets, each saved as a separate text file.


The tool's efficiency and the quality of its output highlight the benefits of incorporating AI into our data analysis processes. While there is a cost associated with ChatGPT Plus, the value it adds, from automating tasks to generating actionable insights, is worth it a look for anyone looking to enhance their data analysis or social media engagement.

Process followed

Upload the file in Data analyst

Get it to read and summarise the work

Get it produce four insights and then get it to produce four Twitter/X posts - you do need to explicitly save twitter posts.


You don't need the paid version to do this but you would need to copy the text in the prompt.

This seems like a simple thing to do, and it is, but it has become one of my favourite uses of ChatGPT and Gemini in part because of that. It is quick you can use it to generate LinedIn posts, etc. Have a go with the free versions of the Generative AI tools.






Labels:

Comments

Post a Comment

Popular posts from this blog

Using an LLM to Find Themes using Thematic Analysis in an Academic Career

B rief recap In an earlier post, “Same prompt, four AIs: why answers aren’t always the same” , I looked at what happened when the same prompt was given to four different LLMs. Unsurprisingly, perhaps, the answers were not identical. That raised an interesting follow-on question: what was the prompt actually trying to do? The answer is that it was trying to support a form of thematic analysis. In that case, the object of analysis was an academic profile, including my Google Scholar profile. The version I discuss here is a modified prompt, used with Claude.ai, where I uploaded a CV and asked the system to find other appropriate public resources connected with a named person and institution. The aim was not simply to summarise the CV, but to identify visible and less visible themes across a whole career. Why use thematic analysis? Thematic analysis is widely used by academics, especially in qualitative research. Braun and Clarke’s well-known paper, Using thematic analysis in psy...
Post a Comment
Read more

Prompt Engineering, Context Engineering and Agentic AI in Higher Education: A Short Briefing

AI in Education Prompt engineering, context engineering and agentic AI are often used interchangeably, but the literature treats them as distinct. Prompt engineering concerns crafting effective single instructions to a model (Glean, 2026). Context engineering is the broader discipline of designing and managing the entire informational environment around a model—memory, retrieval, tool outputs and conversation state—rather than a one-off instruction (Abstracta, 2026); prompt engineering operates within the context window, while context engineering determines what fills it (arXiv:2606.12422, 2026). Agentic AI describes systems that plan and execute multi-step tasks with delegated autonomy, raising organisational questions of accountability rather than purely technical ones (MIT Sloan, 2026; Palo Alto Networks, 2026). There is genuine debate about prompt engineering's durability. IEEE Spectrum (2025) reported research suggesting prompting is increasingly performed by models themse...
Post a Comment
Read more

AI as a Mirror: Transforming Vague Student Ideas into a More Rigorous Project Agreement

The Problem: The "Generic App" and the "Time Sink" We’ve all been there: a student walks into a 1-to-1 with a vague desire to "do something with AI" or "build a fitness app." You spend 45 minutes trying to find a technical "hook" that justifies a Level 6 or Level 7 grade, only for the student to drift back into "CRUD app" territory by week three. The Philosophy: AI as a Mirror Instead of you doing the heavy lifting, this workflow uses AI as a Mirror . It reflects the student’s own skills and career goals back to them, but with the structural rigour of a virtual supervisory team. It’s not about the AI "giving" the idea; it’s about the AI forcing the student to defend and refine their own concepts until they hold water. The Framework: 3 Months of Rigour This prompt is specifically designed for intensive/conversion MSc or summer capstone projects . It assumes a tight 12-week implementation window. By forcing the AI to w...
Post a Comment
Read more