Tag Archives: prompt writing

Identifying bias in your writing — with generative AI

Applications of genAI in the journalism process 
Pyramid with the third 'Production' tier highlighted: 

Identify jargon and bias; improve spelling, grammar, structure and brevity

In the latest in a series of posts on using generative AI, I look at how tools such as ChatGPT and Claude.ai can help help identify potential bias and check story drafts against relevant guidelines.

We are all biased — it’s human nature. It’s the reason stories are edited; it’s the reason that guidelines require journalists to stick to the facts, to be objective, and to seek a right of reply. But as the Columbia Journalism Review noted two decades ago: “Ask ten journalists what objectivity means and you’ll get ten different answers.”

Generative AI is notoriously biased itself — but it has also been trained on more material on bias than any human likely has. So, unlike a biased human, when you explicitly ask it to identify bias in your own reporting, it can perform surprisingly well.

It can also be very effective in helping us consider how relevant guidelines might be applied to our reporting — a checkpoint in our reporting that should be just as baked-in as the right of reply.

In this post I’ll go through some template prompts and tips on each. First, a recap of the rules of thumb I introduced in the previous post.

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Using generative AI to help review your reporting: subbing, jargon, brevity and factchecking

Applications of genAI in the journalism process 
Pyramid with the third 'Production' tier highlighted: 

Identify jargon and bias; improve spelling, grammar, structure and brevity

In the fifth of a series of posts from a workshop at the Centre for Investigative Journalism Summer School, I look at using generative AI tools such as ChatGPT and Google Gemini to help with reviewing your work to identify ways it can be improved, from technical tweaks and tightening your writing to identifying jargon.

Having an editor makes you a better writer. At a basic level, an editor is able to look at your work with fresh eyes and without emotional attachment: they will not be reluctant to cut material just because it involved a lot of work, for example.

An editor should also be able to draw on more experience and knowledge — identifying mistakes and clarifying anything that isn’t clear.

But there are good editors, and there are bad editors. There are lazy editors who don’t care about what you’re trying to achieve, and there are editors with great empathy and attention to detail. There are editors who make you a better writer, and those who don’t.

Generative AI can be a bad editor. Ensuring it isn’t requires careful prompting and a focus on ensuring that it’s not just the content that improves, but you as a writer.

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Using generative AI as a scraping assistant

Applications of genAI in the journalism process 
Research
Pyramid with the second 'research' level highlighted: Scope diverse sources, explore documents, form advanced searches, and write/fix code for scraping and analysis
Scraping is part of the research phase of a project

In the fourth of a series of posts from a workshop at the Centre for Investigative Journalism Summer School (the first part covered idea generation; the second research; the third spreadsheets), I look at using generative AI tools such as ChatGPT and Google Gemini to help with scraping.

One of the most common reasons a journalist might need to learn to code is scraping: compiling information from across multiple webpages, or from one page across a period of time.

But scraping is tricky: it requires time learning some coding basics, and then further time learning how to tackle the particular problems that a specific scraping task involves. If the scraping challenge is anything but simple, you will need help to overcome trickier obstacles.

Large language models (LLMs) like ChatGPT are especially good at providing this help because writing code is a language challenge, and material about coding makes up a significant amount of the material that these models have been trained on.

This can make a big difference in learning to code: in the first year that I incorporated ChatGPT into my data journalism Masters at Birmingham City University I noticed that students were able to write more advanced scrapers earlier than previously — and also that students were less likely to abandon their attempts at coding.

You can also start scraping pretty quickly with the right prompts (Google Colab allows you to run Python code within Google Drive). Here are some tips on how to do so…

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Using generative AI as a spreadsheet and data cleaning assistant

Applications of genAI in the journalism process 
Research
Pyramid with the second 'research' level highlighted: Scope diverse sources, explore documents, form advanced searches, and write/fix code for scraping and analysis
Spreadsheet analysis is part of the research phase of a story

Generative AI tools like ChatGPT and Gemini can be a big help when dealing with data in spreadsheets. In this third of a series of posts from a workshop at the Centre for Investigative Journalism Summer School (the first part covered idea generation; the second research), I outline tips and techniques for using those tools to help with spreadsheet formulae and reshaping data.

Whether you come across data as part of story research, or compile data yourself, chances are that at some point you will need to write a formula to ask questions of that data, or make it possible to ask questions (such as creating a column which extracts data from another).

If you find yourself coming up against the limits of your spreadsheet knowledge, then genAI tools can be useful both in breaking through those — while expanding your knowledge of functions and formula writing.

Writing spreadsheet formulae with ChatGPT or other genAI tools

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Investigative journalism and ChatGPT: using generative AI for sourcing and story research

Applications of genAI in the journalism process 
Research
Pyramid with the second 'research' level highlighted: Scope diverse sources, explore documents, form advanced searches, and write/fix code for scraping and analysis
Generative AI can be used at all points in the journalism process: this post focuses on the research stage

In the second of a series of posts from a workshop at the Centre for Investigative Journalism Summer School (read the first part on idea generation here), I look at using generative AI tools such as ChatGPT and Google Gemini to improve sourcing and story research.

Research is arguably the second-highest risk area (after content generation) for using generative AI within journalism. The most obvious reason for this is AI’s ability to make things up (“hallucinate“) — but there are other reasons too.

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Investigative journalism and ChatGPT: using generative AI for story ideas

Applications of genAI in the journalism process
Pyramid with the bottom 'pre-production' layer highlighted: Idea generation and stimulation: identify and map systems and rules, apply brainstorming frameworks (iceberg model, 5 whys, 8 angles of data journalism). Planning.
Generative AI can be used at all points in the journalism process: this post focuses on pre-production

Last week I delivered a session at the Centre for Investigative Journalism Summer School about using generative AI tools such as ChatGPT and Google Gemini for investigations. In the first of a series of posts from the talk, here are my tips on using those tools for idea generation.

Generative AI tools may not be entirely reliable, but that doesn’t mean that they’re not useful. Journalism, after all, is about more than just gathering information: reporters also need to generate story ideas, identify and approach potential sources, plan ahead, write and edit stories and solve a range of technical challenges. All of these are areas where genAI can help.

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