Prompt-Engineering

In-context learning with GPT-3 and other Large Language Models

Table of contents

• Overview
• Requirements
• Install
• Repository Structure
• Contrbutors

Overview

Large Language Models coupled with multiple AI capabilities are able to generate images and text, and also approach/achieve human level performance on a number of tasks. The world is going through a revolution in art (DALL-E, MidJourney, Imagine, etc.), science (AlphaFold), medicine, and other key areas, and this approach is playing a role in this revolution.

In this project we will systematically explore strategies that help generate prompts for LLMs to extract relevant entities from job descriptions and also to classify web pages given only a few examples of human scores. You will be also required to compare responses and accuracies of multiple LLM models for given prompts. These are the sub taks for this project:

1. Given a news item as json with title, description, and body of the content, return a score between 0 and 10 in one or two significant digits e.g. (1.2 or 0.33).

2. Given a job description text, return the list of entities (and their relationship if possible) extracted from the job description by the LLM model.

Requirements

Python

Pip

Pandas

Sklearn

DVC

Mlflow

Cohere

Install

1.Install the project

git clone https://github.com/gedionabebe/Prompt-Engineering.git
cd Prompt-Engineering
pip install -r requirements.txt

Repository Structure

├── .github/workflows(Github actions)
│   
├── data(Project data)
│   
├── log(Log file)
│
├── notebooks(Jupyter notebooks)
│
├── scripts(Python code)
│
├── tests(Unit tests)
│
├── README.md(Project information)
│
├── requirements.txt(Porject requirements)

Contrbutors

• Gedion Abebe