10 Exciting Project Ideas Using Large Language Models (LLMs) for Your Portfolio

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10 Exciting Project Ideas Using Large Language Models (LLMs) for Your Portfolio

Learn how to build apps and showcase your skills with large language models (LLMs). Get started today!

A data science portfolio is a way to create public evidence of your skills.

One common piece of advice I often hear for job applicants is to have a portfolio showcasing your work. This doesn’t only apply to artists or models but also to software developers and data scientists.

A portfolio of your projects acts as public evidence of your skills. This public evidence can be anything from a blog to open-source contributions to an active engagement on forums such as StackOverflow. But these types of public evidence take a long time to build.

Another type of evidence showcasing your skills is with smaller end-to-end projects.

Another type of evidence showcasing your skills is with smaller end-to-end projects. For data scientists, these can be projects such as exploratory data analysis and data visualization, classical Machine Learning on tabular data, or Deep Learning to classify images.

With the advent of large language models (LLMs) in the form of pre-trained foundation models, such as OpenAI’s GPT-3, the opportunities to build cool things with LLMs are endless. And with the emergence of developer tools, the technical barrier is getting lower.

Thus, now is a great time to add a new LLM-powered project to your portfolio!

This article will share 10 side project ideas that utilize LLMs for downstream tasks. Wherever you are in your career, I hope these will inspire you to build something fun while learning about this new technology.

  1. Cover letter generator
  2. Chatbot with a personality
  3. YouTube summarizer
  4. Information extraction from job postings
  5. Custom web scraper
  6. Searchable database of your documents
  7. Question answering over documents
  8. Clustering social media posts and podcast episodes into topics
  9. Classify business inquiries from e-mails
  10. Where is Waldo?

Projects Based on Text Generation

LLMs are most widely known for their generative capabilities. In this section, we will first discuss some project ideas based on use cases related to them:

  1. Generative: cover letter generator
  2. Conversational: chatbot with a personality
  3. Summarization: YouTube summarizer
  4. Extraction: Information extraction from job postings
  5. Rewriting: Custom web scraper

Project Idea 1: Cover Letter Generator

LLMs’ main superpower is their ability to generate coherent bodies of text. While there is a lot of discussion in the media of how people are exploiting this technology by creating fully AI-generated blogs to students cheating on homework, this technology is already being widely adapted in copywriting or programming to increase productivity.

Did you say you were looking for a new job opportunity? Then this project might be right up your alley: A cover letter generator.

While you could technically build this only by engineering the perfect prompt and filling it with the relevant information about the role, this will become repetitive work if you want to apply for multiple roles.

Thus, this is a great small project to practice prompt engineering and using prompt templates.

prompt_template = """
Write a cover letter to {contact_person}
from {your_name} for a {role} job at {company_name}.
I have experience in {personal_exp}.
I am excited about the job because {job_desc}.
I am passionate about {passion}.

If you are feeling ready for a challenge, you could also try to extract the relevant information from the job posting to feed to the prompt template (see extraction).

Project Idea 2: Customized Chatbot

You’ve heard of ChatGPT. I don’t need to go into detail here. It’s conversational capabilities are pretty impressive. But it lacks personality and has limited information. What if you could give it access to specific knowledge or even a full personality?

The first example is not only a cute and whimsical idea, but it also serves a therapeutic purpose. Michelle Huang built a chatbot based on her diaries to chat with her childhood self.

What’s especially cool about this project is that Michelle used this chatbot for her inner child work.


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