Anticipating Reader Questions
David Colaursso
Co-director, Suffolk's Legal Innovation & Tech Lab
This is the 32nd post in my series 50 Days of LIT Prompts.
One of my favorite bits of writing advice is, "let your writing breathe." That is, don't write everything in one go. Let it sit so you can come back to it with fresh eyes. It's easy to reread something you just wrote and unconsciously fill in the blanks. If you want a different perspective, you need distance or another person. Well, maybe now there's a third option. ;)
Like me, Quinten Steenhuis, with whom I co-directs Suffolk's LIT Lab, has been playing with large language models (LLMs). Recently he wrote some quick thoughts about integrating AI with law school clinical practice in which he had a number of suggestions for how to use an LLM to act as a "editor, critic, or proofreader." Last week we engaged our LLM to do some copy editing, but today I want to focus on something Quinten suggested that really jumped out at me. It jumped out because it provides a new way of accessing the perspective I sought above. How? By answering the question, "What questions would someone have after reading this?"
To use today's template you simply highlight your text and trigger the template. It will then respond with a list of questions your readers might have after reading the text. Some of these you may have answered elsewhere, or they might not be relevant, but hopefully, some of them will make you think, suggesting areas where you might want to strengthen you're argument. Of course, if you aren't making an argument, maybe they'll indicate areas of potential interest or places where you may piqued the reader's curiosity.
FWIW, here's the questions it generated based on the above text.
Taking the above in order:
- To be fair these questions are based only on the above text. So, maybe I should give the GIF a better alt text description, like "a GIF showing Nathan Fillion about to ask a question but then stopping himself." FWIW, I did update the alt text.
- I'm going to say that the link in my byline takes care of this question.
- Again, I'll let the link to the LIT Lab answer this.
- Again, see the link.
- Agreed, but I'm going to lean again on the links (e.g., copy editing and some quick thoughts about integrating AI with law school clinical practice).
- Again, to be fair, I really did rely on links a lot here. See some quick thoughts about integrating AI with law school clinical practice
- Okay, I get it. My writing doesn't work without the links! Here's what you asked for: copy editing
- Finally, something I can answer properly. The step-by-step guide is below, and this right here is the real-world example. :)
- This really is a good question, and if I had more time I'd get to it here, but for now, I'll just have to trust that folks are reading more of the posts in this series as I try to touch upon these issues throughout.
- I really like this one too, but such an examination will have to wait.
Let's build something!
We'll do our building in the LIT Prompts extension. If you aren't familiar with the LIT Prompts extension, don't worry. We'll walk you through setting things up before we start building. If you have used the LIT Prompts extension before, skip to The Prompt Pattern (Template).
Up Next
Questions or comments? I'm on Mastodon @Colarusso@mastodon.social
Setup LIT Prompts
LIT Prompts is a browser extension built at Suffolk University Law School's Legal Innovation and Technology Lab to help folks explore the use of Large Language Models (LLMs) and prompt engineering. LLMs are sentence completion machines, and prompts are the text upon which they build. Feed an LLM a prompt, and it will return a plausible-sounding follow-up (e.g., "Four score and seven..." might return "years ago our fathers brought forth..."). LIT Prompts lets users create and save prompt templates based on data from an active browser window (e.g., selected text or the whole text of a webpage) along with text from a user. Below we'll walk through a specific example.
To get started, follow the first four minutes of the intro video or the steps outlined below. Note: The video only shows Firefox, but once you've installed the extension, the steps are the same.
Install the extension
Follow the links for your browser.
- Firefox: (1) visit the extension's add-ons page; (2) click "Add to Firefox;" and (3) grant permissions.
- Chrome: (1) visit the extension's web store page; (2) click "Add to Chrome;" and (3) review permissions / "Add extension."
If you don't have Firefox, you can download it here. Would you rather use Chrome? Download it here.
Point it at an API
Here we'll walk through how to use an LLM provided by OpenAI, but you don't have to use their offering. If you're interested in alternatives, you can find them here. You can even run your LLM locally, avoiding the need to share your prompts with a third-party. If you need an OpenAI account, you can create one here. Note: when you create a new OpenAI account you are given a limited amount of free API credits. If you created an account some time ago, however, these may have expired. If your credits have expired, you will need to enter a billing method before you can use the API. You can check the state of any credits here.
Login to OpenAI, and navigate to the API documentation.
Once you are looking at the API docs, follow the steps outlined in the image above. That is:
- Select "API keys" from the left menu
- Click "+ Create new secret key"
On LIT Prompt's Templates & Settings screen, set your API Base to https://api.openai.com/v1/chat/completions and your API Key equal to the value you got above after clicking "+ Create new secret key". You get there by clicking the Templates & Settings button in the extension's popup:
- open the extension
- click on Templates & Settings
- enter the API Base and Key (under the section OpenAI-Compatible API Integration)
Once those two bits of information (the API Base and Key) are in place, you're good to go. Now you can edit, create, and run prompt templates. Just open the LIT Prompts extension, and click one of the options. I suggest, however, that you read through the Templates and Settings screen to get oriented. You might even try out a few of the preloaded prompt templates. This will let you jump right in and get your hands dirty in the next section.
If you receive an error when trying to run a template after entering your Base and Key, and you are using OpenAI, make sure to check the state of any credits here. If you don't have any credits, you will need a billing method on file.
If you found this hard to follow, consider following along with the first four minutes of the video above. It covers the same content. It focuses on Firefox, but once you've installed the extension, the steps are the same.
The Prompt Pattern (Template)
When crafting a LIT Prompts template, we use a mix of plain language and variable placeholders. Specifically, you can use double curly brackets to encase predefined variables. If the text between the brackets matches one of our predefined variable names, that section of text will be replaced with the variable's value. Today we'll be using {{highlighted}}. See the extension's documentation.
The {{highlighted}} variable contains any text you have highlighted/selected in the active browser tab when you open the extension. To use our prompt template, select the text on a webpage that you want to run through the template and trigger the template from the extension. It will return a list of questions based on a "reading" of that text.
Here's the template's title.
What questions might readers have?
Here's the template's text.
You're a helpful editor, and you're going to help me with a writing project. For the text that follows, what unanswered questions might the reader have?
{{highlighted}}
And here are the template's parameters:
- Output Type:
LLM. This choice means that we'll "run" the template through an LLM (i.e., this will ping an LLM and return a result). Alternatively, we could have chosen "Prompt," in which case the extension would return the text of the completed template. - Model:
gpt-4o-mini. This input specifies what model we should use when running the prompt. Available models differ based on your API provider. See e.g., OpenAI's list of models. - Temperature:
0.7. Temperature runs from 0 to 1 and specifies how "random" the answer should be. Here I'm using 0.7 because I'm happy to have the text be a little "creative." - Max Tokens:
500. This number specifies how long the reply can be. Tokens are chunks of text the model uses to do its thing. They don't quite match up with words but are close. 1 token is something like 3/4 of a word. Smaller token limits run faster. - JSON:
No. This asks the model to output its answer in something called JSON. We don't need to worry about that here, hence the selection of "No." - Output To:
Screen Only. We can output the first reply from the LLM to a number of places, the screen, the clipboard... Here, we're content just to have it go to the screen. - Post-run Behavior:
CHAT. Like the choice of output, we can decide what to do after a template runs. Here we want to be able to follow up with additional prompts. So, "CHAT" it is. - Hide Button:
unchecked. This determines if a button is displayed for this template in the extension's popup window.
Working with the above template
To work with the above template, you could copy it and its parameters into LIT Prompts one by one, or you could download a single prompts file and upload it from the extension's Templates & Settings screen. This will replace your existing prompts.
You can download a prompts file (the above template and its parameters) suitable for upload by clicking this button:
Kick the Tires
It's one thing to read about something and another to put what you've learned into practice. Let's see how this template performs.
- Umm... wait? Instead of assuming a generic audience, tweak the prompts to simulate a specific audience. For example add some details about your readers (e.g., they are first year law students). See how this changes the questions the template produces.
TL;DR References
ICYMI, here are blubs for a selection of works I linked to in this post. If you didn't click through above, you might want to give them a look now.
- Some quick thoughts about integrating AI with law school clinical practice by Quinten Steenhuis. I co-direct the LIT Lab with Quinten and really appreciate his take on the use of AI in law school clinics. He believes that law school clinics should be using generative AI tools, but acknowledges that it requires careful thought and planning. Steenhuis suggests several safe uses for AI in clinical education, such as solving the blank page problem, brainstorming, extracting information, classifying, editing, translating, and simplifying. He also addresses concerns about teaching generative AI, including the risk of automation bias and perpetuating biases. Steenhuis emphasizes the importance of teaching students how to critically evaluate AI output and suggests integrating AI lessons into existing curriculum. He concludes by stating that generative AI has practical uses and ignoring it in clinical practice will put law students at a disadvantage. Summary based on a draft from our day one template.