Amber Teng - Building apps with a new generation of language models

2022-10-05 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, extended

Summary

Amber Tang, a data scientist, developed a resume cover letter generator using OpenAI's GPT-3, specifically the DaVinci model, which she deployed on Streamlit. This project, her first with a large language model, took approximately five hours to build, including three hours for learning OpenAI's API and one to two hours for Python coding and Streamlit deployment. The application allows users to input company name, role, contact person, their name, personal experience, job description interests, and passions to generate customized cover letters. Tang's experience highlights the ease and speed with which powerful AI applications can now be developed, contrasting sharply with traditional machine learning development. The tool's usage costs are minimal, with 46 requests costing only 64 cents, demonstrating the economic accessibility of such AI-powered solutions.

Key takeaway

For data scientists or developers looking to rapidly prototype AI-powered applications, you should prioritize leveraging pre-trained large language models and accessible deployment platforms. This approach significantly reduces development time and cost, allowing you to quickly validate use cases and iterate on prompt engineering. Be mindful of model limitations, such as context window and potential for factual inaccuracies, and consider fine-tuning for higher quality, domain-specific outputs.

Key insights

Modern language models enable rapid development of powerful AI applications with minimal coding and cost.

Principles

• Iterative prompt refinement improves AI output quality.
• Model parameters like "temperature" control creativity vs. accuracy.
• Project-based learning accelerates understanding of complex AI concepts.

Method

Start with simple prompts, gradually adding detail. Experiment with model parameters (e.g., temperature, frequency penalty) to balance creativity and coherence. Deploy quickly using tools like Streamlit to de-risk development.

In practice

• Use OpenAI's Playground for initial prompt testing.
• Consider DaVinci for longer, more creative text generation.
• Explore text summarization for managing token limits with user inputs.

Topics

• Large Language Models
• Prompt Engineering
• AI Application Development
• GPT-3
• AI Ethics

Best for: Data Scientist, Machine Learning Engineer, Prompt Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.