Recruiter's Glossary

Examples: Classification • Cross-Validation • Data Preprocessing

GPT

Term from Data Science industry explained for recruiters

GPT (which stands for Generative Pre-trained Transformer) is a modern type of artificial intelligence technology that can understand and generate human-like text. Think of it as a very advanced language tool that can write, answer questions, and help with various text-related tasks. It's similar to having a smart assistant that can understand context and generate relevant responses. Companies use GPT technology to automate customer service, create content, analyze text data, and build AI-powered applications. You might see it mentioned alongside terms like "natural language processing" or "language model." GPT has become particularly well-known through products like ChatGPT, GPT-3, and GPT-4.

Examples in Resumes

Developed customer service chatbot using GPT technology, reducing response time by 60%

Implemented GPT-3 and GPT-4 models for automated content generation

Created data analysis pipeline utilizing GPT for text processing and insights

Typical job title: "AI Engineers"

Also try searching for:

Machine Learning Engineer AI Developer NLP Engineer Data Scientist AI Research Engineer Prompt Engineer AI Application Developer

Where to Find AI Engineers

Online Communities

Job Boards

Professional Networks

Example Interview Questions

Senior Level Questions

Q: How would you approach implementing GPT models in a production environment?

Expected Answer: A senior candidate should discuss considerations like cost management, API integration, handling high traffic, backup systems, and monitoring performance. They should also mention safety measures and ethical considerations.

Q: What strategies would you use to optimize GPT model performance while managing costs?

Expected Answer: Look for answers about efficient prompt engineering, caching strategies, batch processing, and smart usage of different model sizes based on task requirements. They should also mention monitoring and optimization techniques.

Mid Level Questions

Q: How would you ensure the quality and safety of GPT-generated content?

Expected Answer: Candidate should discuss content filtering, input validation, output verification, and implementing safety measures to prevent inappropriate or biased content. They should also mention testing and monitoring strategies.

Q: Explain how you would handle GPT API rate limits and errors in a production application.

Expected Answer: Should discuss implementing retry mechanisms, queue systems, error handling, and fallback options. They should also mention monitoring and alerting systems.

Junior Level Questions

Q: What is prompt engineering and why is it important when working with GPT?

Expected Answer: Should explain that prompt engineering is about crafting effective instructions for GPT models to get desired results, and demonstrate basic understanding of how to write clear prompts.

Q: How would you integrate GPT into a simple application?

Expected Answer: Should be able to explain basic API usage, handling responses, and implementing simple error handling. Basic understanding of working with GPT endpoints is expected.