Develop the critical skill that DataCamp's 2026 research identifies as the real bottleneck: evaluating whether AI output is accurate, useful, and safe to act on.
Before
Ask an AI to write a short summary of a topic you know well — your company's product, your field of expertise, a hobby you are deeply knowledgeable about. Read the output and mark every statement that is slightly wrong, oversimplified, or missing important nuance. Count the issues. This exercise calibrates your sense of how much you should trust AI in domains you know less well.
After
DataCamp's 2026 research shows that the bottleneck in AI value is not generating outputs — it is evaluating them. Organizations with structured evaluation skills see nearly double the AI ROI. The skill is not 'how to prompt better' but 'how to judge whether this output is trustworthy enough to act on.'
Tip
Be specific about what you need. The more context you provide, the better the result.
Your result will appear here.
Why AI Output Evaluation Is the Real Skill
Explain why evaluation — not prompting — is the primary AI skill gap in 2026
Evaluation Techniques by Output Type
Apply fact-checking workflows and source verification to evaluate AI-generated text
Building Evaluation into Your Workflow
Implement the VERIFY framework for systematic AI output evaluation