Natural Language Generation
Natural Language Generation (NLG) is a technology that helps computers create human-like text automatically. Think of it as teaching computers to write like people do. It's used to automatically create things like reports, product descriptions, or personalized emails at scale. This technology is part of the larger field of artificial intelligence and is similar to other text-focused AI tools. When you see job candidates mentioning NLG, they're typically talking about building or working with systems that can turn data or information into readable text that sounds natural to humans.
Examples in Resumes
Developed Natural Language Generation systems to automate report writing for financial clients
Improved accuracy of NLG models for customer service chatbots
Led team implementing Natural Language Generation solutions for content automation
Typical job title: "NLG Engineers"
Also try searching for:
Where to Find NLG Engineers
Online Communities
Professional Networks
Conferences & Events
Example Interview Questions
Senior Level Questions
Q: How would you approach building a system that generates human-like product descriptions from basic product data?
Expected Answer: A senior candidate should explain the process of analyzing input data, designing templates, ensuring output quality, and implementing ways to maintain consistent brand voice. They should mention handling edge cases and quality control measures.
Q: What methods would you use to evaluate the quality of generated text?
Expected Answer: The answer should cover both automated metrics and human evaluation, discussing ways to measure readability, accuracy, and naturalness of the text. They should mention practical quality assurance processes.
Mid Level Questions
Q: How do you ensure the generated text remains consistent with the brand voice?
Expected Answer: Should explain approaches to maintaining style guidelines, using templates, and implementing controls to keep output aligned with desired tone and voice.
Q: What challenges have you faced when implementing NLG systems?
Expected Answer: Should discuss practical problems like handling unusual data, maintaining text quality, and managing client expectations, along with solutions they've implemented.
Junior Level Questions
Q: What is Natural Language Generation and how is it different from chatbots?
Expected Answer: Should be able to explain that NLG focuses on creating coherent text from data, while chatbots focus on dialogue. Should demonstrate basic understanding of text generation concepts.
Q: What are some common applications of NLG?
Expected Answer: Should mention practical examples like automated reporting, product descriptions, or personalized emails, showing understanding of basic use cases.
Experience Level Indicators
Junior (0-2 years)
- Basic understanding of text generation concepts
- Working with existing NLG templates
- Basic data processing
- Quality checking of generated content
Mid (2-5 years)
- Creating and improving NLG templates
- Implementation of text generation systems
- Content quality optimization
- Integration with other systems
Senior (5+ years)
- Advanced NLG system architecture
- Large-scale content generation management
- Team leadership and project planning
- Complex system optimization
Red Flags to Watch For
- No understanding of basic text generation concepts
- No experience with content quality assessment
- Lack of attention to detail in written communication
- No awareness of ethical considerations in AI text generation
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