Generative AI: Validating ResultsGenerative AI: Validating Results is an intermediate course designed to help professionals get better, more accurate outcomes from generative AI.
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- Spot and fix common AI issues like hallucinations, errors, and bias
- Use prompts and frameworks to validate AI output with accuracy and speed.
- Apply a 4-step process to evaluate and improve your AI responses.
- Build quality control into your AI workflow to scale reliable results.
- Learn how to prompt for sources, reasoning, and validation.
- Use human review techniques to ensure high-quality and trustworthy content.
What’s Included In The Course
- Downloadable Resources
- Completion Certificate
- Live Demonstrations
- Activities to Implement
- Real-world Examples
Resources
-> AI Result Validation Cheat Sheet
Validating AI results is crucial to ensure that you create accurate, high-quality, relevant, and reliable results. Even the best prompts are prone to issues with results. This course teaches you how to identify common issues with AI and resolve them. Discover how to validate your AI results, prompt for validation, and build quality control into your prompts and workflows.
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Generative AI: Validating Results Course Outline
- Common risks and mistakes AI makes.
- How to spot and avoid common AI results issues.
- How to evaluate AI responses with a clear framework.
- How to check for accuracy, completeness, objectivity, relevance, and transparency.
- How to spot common AI issues in responses.
- How to prompt AI to resolve AI issues.
- How to prompt to validate AI results.
- How to adapt your prompts to increase accuracy.
- Prompt strategies that increase accuracy and quality of results.
- Why human validation is necessary when quality outputs matter.
- Different human validation strategies to apply to improve results.
- How to prompt for sources, criteria, and reasoning to validate results.
- How to evaluate and refine the sources, criteria and reasoning used by AI.
- How to validate AI results beyond accuracy.
- A four-step method to validate AI results.
- How to analyze results to determine what to validate.
- How to craft specific prompts to validate.
- Importance of having quality control in your workflows.
- Different strategies to use to validate outputs.
Course Description
Generative AI: Validating Results is an intermediate course designed to help professionals get better, more accurate outcomes from generative AI. Even with the best prompts, AI can produce flawed results. This course shows you exactly how to evaluate AI outputs and apply a process to validate and improve the
You’ll start by understanding the most common AI risks and mistakes — including hallucinations, factual errors, and bias — and how to recognize them in your outputs. You’ll learn how to use a clear evaluation framework to check for accuracy, completeness, objectivity, relevance, and transparency.
Next, discover how to spot specific issues like hallucinations and errors, and how to write validation prompts to address them. Learn proven prompt strategies that improve AI accuracy and result quality. You’ll also discover how to refine your initial prompts and build validation into the process from the beginning.
The course includes live demonstrations to show you how to implement human validation techniques effectively. You’ll explore strategies for prompting AI to provide sources, decision criteria, and reasoning. These steps help you not only validate content but also assess the trustworthiness of the AI’s logic.
You’ll get a repeatable four-step process to validate AI results beyond basic fact-checking — including how to analyze your outputs, identify gaps, and refine them for clarity and credibility. Learn how to direct AI in exactly what to validate and how to build validation into your prompts.
Finally, you’ll build a quality control workflow. You’ll explore practical strategies to integrate validation into your AI systems and scale outputs with accuracy and confidence.
At the end of the course, you’ll have a complete toolkit to evaluate, validate, and trust your AI results.
This course includes the AI Result Validation Cheat Sheet to implement your learnings and build smarter, more reliable AI workflows.
Learning ObjectivesIdentify common AI mistakes and prompts to resolve them.
- Apply an AI results evaluation framework to ensure quality results.
- Spot common AI results issues like hallucinations, errors, or bias.
- Validate your results with prompts.
- Refine your initial prompts to increase accuracy.
- Apply human validation techniques to ensure quality and accuracy.
- Request sources, criteria, and reasoning from AI.
- Apply the steps in the AI validation process.
- Provide clear and specific directions to AI in validating results.
- Build quality control into your workflows to scale AI accurately.
Resources
- AI Result Validation Cheat Sheet
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Krista NeherDigital Marketing Expert & Best-Selling Author
Krista Neher is a six-time bestselling author, international speaker, award-winning entrepreneur, global thought-leader, and an expert in social media, digital marketing, and personal branding, with over 20 years of experience.
She is the CEO of Boot Camp Digital and a contributor on Forbes and Newsweek. She wrote the first textbook on social media and created the first accredited digital marketing certification in the world.
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