Janani Sekar:
Okay. Hi everyone. Welcome to the eighth and final video in this series. You made it to the end. We're just going to do some quick review of what we've covered and some takeaways. Hopefully you understand why we should care about AI, LLMs and Agentic tools. You understand ways in which they can improve our research output and our productivity. You understand how machine learning methods, things like unstructured data can be incorporated into the type of research that we do. It says economics research here, but you know I more broadly mean business, statistics, any type of work that involves running models and crunching numbers. And you also at the same time see the caveats about using LLMs and AI agents in day-to-day work. So when we're using language models and generative AI to label our data, that might be biased. When we use these models to interpret our results, they might overstate our conclusions.

So all things to bear in mind. Hopefully you understand some technical concepts like APIs, how language models actually work. You remember some of those prompt engineering best practices. No worries if you don't remember that all of the references will be included at the end of this module. You understand when to use agents and when you don't actually need an agent. Remember that if you're just asking a language model about some mathematical concept that you're trying to review that a language model you have reason to believe knows very well, you don't need to build out a custom agent for that. There are different types of agents. We talked about deep research. We talked about vibe coding tools. We talked about creative agents like Lovable or Gamma, and we talked about your own custom tools as well. Some parting advice I've got for you, remember that verification is always non-negotiable, especially because we are predominantly in this augmented task space as opposed to this automated task space.

Agents can accelerate our work, but they will never replace our judgment. Actually, I'll never say never, at least in the short run, they will not replace our judgment. We should not treat agents as block boxes. We shouldn't just ask them questions, take the results and go forward with them. It is on us to know what they do, especially when we're building our own vibe coded agents. We can easily see that when we look at our code, like that CoLab notebook we looked at in the last module. When we vibe code in general, we can ask the agent to tell us what it's doing at each step of the way so that we understand the processing steps that it's taking. Know that prompt engineering matters. What you ask your agent, how you ask your agent that question, the order in which you show it, examples, the roles that you tell it to take on, all of these things affect the quality of our results.

Remember to choose the right tool. Remember when you're doing deep research, there was that table from CoreNet25 of all of the different deep research agents you could choose from. Remember that not all tasks will require agents. Remember that there are cases where you want to vibe code your own agent, put in that fixed cost versus cases when you don't want to. And remember to always be transparent and use good research practices, document AI assistance in your work. Always tell your PI if you used AI to prepare some memo so that they are not accountable for your AI use. Be clear about what the agents did when you're presenting anything versus what you used your human judgment for. This is that set of references. I just wanted to thank you all for being here with me. Shout out to all of these folks once again. I hope you learned something from this series.

I hope that you are curious to read and learn more about some of these incredible tools. There is a new language model or agent on the internet and on Twitter and on the market pretty much every day these days. And it's really great to be able to stay on top of this stuff and be able to participate in conversations about where this field is heading. I think it's a very exciting time to be a pre-doc and hopefully you do too. And thanks again for being here. Hope you enjoyed it.