Janani Sekar:

Hi everyone, and welcome to video six. This module is going to be all about vibe coding for researchers. We spent a small portion of the previous video, I know, discussing how to vibe code. This video is going to be a little bit more guiding you through workflows and examples where you can use coding agents in your work. Before that though, I also know that some of you have told me that you are nervous about presenting AI-generated work in a professional context or to your PI or to an advisor. I totally understand that. If that is the case and you're not yet at the point where you feel comfortable in your ability to validate agentic outputs or vibe code something that you would present professionally, I do not want you to do that. I do, however, want to give you a few examples of places that you can consider vibe coding outside of your professional life to just try and gain some familiarity and comfort with doing this because I think it's such a fun and accessible way to use agentic tools.

So we'll start with personal academic websites. I know I've mentioned this a few times at this point throughout this video series. One way to do this is to have a coding agent build a personal website for you. Another way to do it is actually to use one of the creative tools that I mentioned above. One of those creative agents like Lovable or Gamma. They're actually capable of generating websites as well without you even having to check the code. So all of the code there is masked behind the hood. The interface works with a chatbot where you tell it what your website preferences are and it'll kind of iterate with you until it gives you a website that you are semi-satisfied with. The only thing that I'll say is that they will usually cost somewhere around 10 to $20 a month, typically a subscription-based model versus vibe coding agents do have free tiers.

And so if you're willing to try and inspect the code yourself, doing this purely with a coding agent might be a more economical way to approach this. Experiment interfaces, this is one that I want to spend a few minutes talking about. Personally, this is where most of my vibe coding experience professionally has been. Particularly if you are bored of Qualtrics and you do have to make surveys for work and you are tired of that drag and drop survey flow interface or that randomization logic that is difficult and annoying to implement, I do highly encourage you to try using another platform, a maybe more coding heavy platform that doesn't have to feel so coding heavy anymore because you have an agentic interface like Cursor or Claude Code to guide you through it. So two of these platforms are Streamlit and OTree. These are both Python libraries for building interactive websites.

OTree is actually designed by researchers, I think psychology researchers, if I'm not mistaken. And so it is specifically made for doing interactive decision-making tasks. So playing prisoners dilemmas with two people or having two people talk to each other, teacher-learner type of interactions. OTree is going to be really great for that. Streamlit is another more generalizable library in Python for building interactive websites, and it integrates well with Prolific and MTurk. Both of these tools do in my experience, and I absolutely recommend that you test them out. Again, if you're nervous about doing this professionally, feel free to take an old Qualtrics survey that you might've programmed and try to get an agent to translate it into Streamlit or OTree using Python for you, see how well that works, and if that's something you're willing to try professionally in the future after that. Data dashboards are another really great application.

These render very, very nice to look at figures that are also interactive. So figures that you can click into to see the numbers or to see the breakdown or the distribution and Streamlit. The same platform I mentioned above for experimental interfaces also works for building interactive dashboards. There is an equivalent in R called Shiny. And I've seen a lot of people actually take papers that they really like and try to summarize the results of that paper in a more user-friendly, more general audience way using one of these data dashboards that they vibe coded with a single natural language prompt to a coding agent. And finally, I'm going to be a little bit repetitive because I know I mentioned in the last module that we would talk about building custom coding agents at some point. We're not going to do that just yet, but skip ahead to the next video if you are itching to find out how we might be able to do that.

Okay. What we'll do now is we will talk through an agentic workflow for some tasks that might be relevant to a researcher like me or you. And let's set the scene. We have some survey data that we've collected and we want to analyze the effect of some stimulus. So we had a treatment group that saw the stimulus in a control group that did not. And we want to explore our data and maybe get some summary statistics, validate the data, see what's going on here, make sure that we do some quality control, and we can write a detailed prompt to our agent, our coding agent, to tackle this task. We don't actually have to paste in the data itself because the agent has access to the file system on your computer. This is definitely true if you use a tool like Cursor or ClaudeCode or GitHub Copilot. 

The agent is going to run this code. You should, of course, at every step of the way, be checking the outputs, making sure that they make sense, and then you'll move into asking the agent to do your primary analysis, which will actually be running some sort of economic model, estimating some parameter. And once again, at every step of the way, you should be validating the results, asking the agent for explanations for the code that it is writing and making sure that what you are seeing is in line with what you are expecting to see. And then once you've got your preliminary analysis done, maybe you've ran a model or two, you've got a table with those results, you might want to do some additional robustness checks or validation, maybe add some covariates, include fixed effects. And this is also something that your agent should be able to build on with very limited prompting. 

Finally, you can also ask your agent to package your results. It should be able to take the results from your regression tables or from the code to run those models and turn those into figures based off of your preferences for color, axis labels and the like. Maybe not quite for a paper, but definitely good enough to show your PI. Okay. So suppose that I want to create an online behavioral experiment where I am studying how people make decisions under uncertainty. I'm going to do this in Streamlit as opposed to in Qualtrics. And this is a true experiment that I program. So this is a real prompt that I used and I had to trial an error with the prompt a little bit, but this was enough to get me a skeleton of this project. And then I went through and I changed a few things around.

So I said, I wanted a consent form, an instructions page, practice rounds, and a main experiment. We wanted participants to see two lotteries with probabilities and payoffs. We wanted to allow the participant to choose one of those payoffs, show them the outcome, and also store variables in my database, including what choice they made, how long it took them to make that choice and things like that. And eventually I had to go back and add some constraints to this because you can see how this part of the prompt is the most general and maybe not fleshed out. So we want to limit the payoffs to a certain range. We want the probabilities to not be entirely random. We want them to come from some distribution. Things like this, you do have to go back and workshop, but a prompt like this will be more than enough to get you set up with the skeleton of an online experiment in Streamlit.

At that point, you might not even need the agent to make those edits if you gain some sense of what's going on in that code, you can make those edits yourself.

One thing that I wanted to flag is that sources of unstructured data like text and images can be very, very powerful inputs to economic models. So even in a standard regression, if you have a way to numerically represent an image, that is something that you can include in the right-hand side of your model. One big barrier though to using these data sources is that processing unstructured data typically requires advanced computational knowledge. Oftentimes we use neural networks to turn text or images into vectors, into numbers that we can use in economic models. One advantage of vibe coding is that this type of data transformation is no longer off limits to someone that doesn't have that computational knowledge. So the ability to vibe code just strips away this computational overhead because you can just vibe code the pipeline that takes your image, your unstructured data, and gets you some tabular structure.

And so one example application might be that you have satellite imagery of agricultural areas, you want to predict crop yields, you can vibe code a processing pipeline to turn those images into structured data that you can now include in an economic model. So the main takeaway that you should have for this module is that coding agents are not going to replace human expertise. When you vibe code an application, when you vibe code a model, a data pipeline, you'll still need to understand what's happening at every step of the way and take the steps to make sure that you understand what's happening every step of the way. The primary thing that this is going to save you is all of that time and energy that you would've needed to learning and understanding and familiarizing yourself with syntax. And so one perspective that I was given, and I don't know how true this is for you, but to treat the agent as an undergrad RA.

So it is going to help you get your tasks done, but no way is it going to be able to replace your expertise and your understanding of the problem that you're working on.