Learn / AI Training Foundations / Module 1 · 9 min
AI Training: Roles, Tasks & Expectations
Why humans are still part of AI development
AI systems can generate fluent text, code, summaries, images, and analyses, but fluency is not the same as quality. Human evaluators help determine whether outputs are useful, accurate, relevant, safe, and aligned with instructions.
Common human-feedback tasks
- Choose the stronger of two responses
- Rate an answer against a rubric
- Identify instruction violations
- Flag factual or unsupported claims
- Rewrite a weak answer
- Label or categorize content
- Evaluate specialist work such as coding, math, science, finance, or language
Evaluator vs. engineer
AI evaluator / trainer
- Judgment and comparison
- Reading and writing
- Research when permitted
- Applying rubrics consistently
- Explaining decisions
AI engineer
- Software development
- Model/API integration
- Data or ML pipelines
- Infrastructure and deployment
- Experimentation and metrics
The task is usually narrower than the whole product
An evaluator may be asked to judge one response using one rubric. The goal is not to redesign the AI system; it is to make a careful decision within the task instructions.
Prompt
Give me three inexpensive vegetarian dinner ideas.
Response
1. Chicken tacos 2. Vegetable pasta 3. Lentil soup
The response is well formatted, but chicken violates the vegetarian requirement. A polished response can still fail the task.
Module summary
- Human evaluation focuses on output quality and instruction compliance.
- Different projects require different expertise.
- Strong writing does not guarantee factual or instructional correctness.
- Project availability can change even after onboarding.
Quick check
Which is the best example of an AI-evaluation task?
Quick check
Does every AI-training project require programming?
Quick check