Tag Archives: prompt design

Parallel prompting: another way to avoiding deskilling with AI

Train tracks
Photo by Markus Winkler

Too often discussion around using AI is “either/or” — an assumption that you either use AI for a task, or do it yourself. But there’s another option: do both.

Parallel prompting“* is the term I use for this: while you perform a task manually, you also get the AI to perform the same task algorithmically.

For example, you might brainstorm ideas for a story while asking ChatGPT to do the same. Or you might look for potential leads in a company report — and upload it to NotebookLM to perform the same task. You might draft an FOI request but get Claude to draft one too, or get Copilot to rewrite the intro to a story while you attempt the same thing.

Then you compare the results.

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“I don’t want it to be easy” and other objections to using AI

In September I took part in a panel at the African Journalism Education Network conference. The most interesting moment came when members of the audience were asked if they didn’t use AI — and why.

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How to stop AI making you stupid: hybrid destination-journey prompting

A local map-style illustration where a pinned "answer" destination is visible, but the route is overlaid with checkpoints labelled “confidence”, “sources”, “counter-arguments”, “verify”, “edit” (image generated by ChatGPT).

Last month I wrote about destination and journey prompts, and the strategy of designing AI prompts to avoid deskilling. In some situations a third, hybrid approach can also be useful. In this post I explain how such hybrid destination-journey prompting works in practice, and where it might be most appropriate.

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How to ask AI to perform data analysis

Consider the model: Some models are better for analysis — check it has run code

Name specific columns and functions: Be explicit to avoid ‘guesses’ based on your most probably meaning

Design answers that include context: Ask for a top/bottom 10 instead of just one answer

'Ground' the analysis with other docs: Methodologies, data dictionaries, and other context

Map out a method using CoT: Outline the steps needed to be taken to reduce risk

Use prompt design techniques to avoid gullibility and other risks: N-shot prompting (examples), role prompting, negative prompting and meta prompting can all reduce risk

Anticipate conversation limits: Regularly ask for summaries you can carry into a new conversation

Export data to check: Download analysed data to check against the original

Ask to be challenged: Use adversarial prompting to identify potential blind spots or assumptions

In a previous post I explored how AI performed on data analysis tasks — and the importance of understanding the code that it used to do so. If you do understand code, here are some tips for using large language models (LLMs) for analysis — and addressing the risks of doing so.

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7 prompt design techniques for generative AI every journalist should know

Tools like ChatGPT might seem to speak your language, but they actually speak a language of probability and educated guesswork. You can make yourself better understood — and get more professional results — with a few simple prompting techniques. Here are the key ones to add to your toolkit. (also in Portuguese)

Prompt design techniques for genAI
Role prompting
One-shot prompting
Recursive prompting
Retrieval augmented generation
Chain of thought
Meta prompting
Negative prompting
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