AI and LLM Project#
In this document, we will guide you through three key considerations for implementing your project in this course: choosing a project, defining acceptable outputs, and understanding the project selection process.
Recommended Projects#
This section addresses the question, “What project can I do?” Based on the course content, we provide recommended projects, but you are encouraged to explore other ideas.
QA Chatbots#
One common use case for generative AI is creating conversational systems like chatbots that can answer questions on specific topics. While chat-GPT and other models can handle general questions, they lack access to custom organizational data. For example, in the health domain, you may want to create a chatbot that answers questions on public health issues in your country. By using LLMs, you can create custom chatbots with access to specialized documents or websites for local knowledge.
Note: For QA chatbots, we will focus on those that respond to textual questions rather than numeric or data-intensive information.
Statsbots#
Similar to the QA chatbot, a Statsbot is designed to answer quantitative questions. LLMs traditionally struggle with numeric data, so specialized tools are necessary for chatbots that work with tabular data and provide accurate, data-driven answers.
Miscellaneous Document Analysis#
LLMs are highly effective for analyzing documents, classifying them, and performing various NLP tasks. Examples of document analysis projects include:
Sentiment Analysis
Topic Classification
Intent Classification
Named Entity Recognition (NER)
Document Type Classification
Key Phrase Extraction
Toxicity and Hate Speech Detection
Guidelines for Choosing a Project#
Select your project thoughtfully, given the limited time available. Here are factors to consider:
Data Availability: Ensure that the necessary data or documents are accessible for the project.
Skills and Knowledge: Assess the required platforms or tools and confirm that team members are willing to learn and work with them.
Effort: Be realistic about the project’s scope. Certain tasks, like fine-tuning an LLM, may require additional time and resources.
Cost: Some LLM platforms and tools may require subscriptions or fees. For example, using the chat-GPT API requires a developer account with sufficient funds. While paid platforms are sometimes necessary, ensure that you understand the associated requirements.
Permissible Project Outputs#
We recommend including three key components as project outputs:
User Interface
Implementing LLMs often involves facilitating user interaction with documents, data, or other elements. For a more user-friendly experience, we suggest creating a user interface, such as a web-based UI, WhatsApp chatbot, or command-line tool.Documentation on GitHub
As this is a technical project, you’ll write substantial code. Using a version control system like GitHub is recommended to track yo