Director, Learning Design & Development| PMIAsheville, NC, United States
Validating and checking outputs is critical when working with AI systems like Generative AI. Such validation approaches may include establishing clear criteria, implementing strong testing protocols, and continuous refinement.
In your experience with AI, what are some best practices for ensuring the results you receive are accurate, relevant, and aligned with your original goals?
Diana GarciaSenior Analyst and Developer| Deacero S.A.P.I.Monterrey, Nuevo León, Mexico
By using some formula like RTF or CREATE. But also iterating for refining the prompts and responses of the LLM. Saving Changes...
Anonymous
Monitor and control.
Be detailed. Specific. Add examples.
Then look at results just as we have always done by Monitoring and Controlling our work.
Saving Changes...
Elizabeth MassuraPrincipal Marketo Consultant| AcxiomChicago, IL, United States
I don't have much AI experience yet, but it's important to include the context of your business, your industry, your known concerns, your goals, etc., in your prompt. That will help the output be more relevant. Saving Changes...
I believe it starts with the request. Ensure the request is specific and clear about end goals. This AI's response will likely be more aligned to your goals if the are clearly defined upfront or in iterative discussions. Saving Changes...
When managing projects that involve the use of AI systems, it's crucial to implement best practices to ensure the results are accurate, relevant, and aligned with project goals. Here are some effective strategies:
1. Define Clear Objectives and Success Metrics
Set Specific Goals: Clearly outline what you want to achieve with the AI system. Establish measurable outcomes and performance indicators that align with your project’s objectives.
Determine Quality Standards: Define the criteria for accuracy and relevance upfront, and ensure they are well-understood by the team and reflected in the AI system’s configuration.
2. Use High-Quality, Relevant Data
Data Preprocessing: Ensure the input data is clean, consistent, and representative of the problem you are trying to solve. Address issues like missing data, outliers, and data imbalances.
Regular Data Updates: Keep your data sets up to date to maintain the relevance of the AI model. Outdated data can lead to inaccurate or misleading results.
3. Implement Robust Testing and Validation
Cross-Validation: Use methods like cross-validation to test the AI model across different subsets of the data, ensuring the results are generalizable.
A/B Testing: Compare the AI model’s performance against a control or different versions of the model to validate its effectiveness.
4. Monitor and Audit the AI System Continuously
Performance Monitoring: Track key metrics over time to identify any drift in the model’s performance or relevance. This helps ensure the AI system continues to deliver high-quality results.
Error Analysis: Regularly review errors or unexpected outputs to understand their root causes and make necessary adjustments.
5. Incorporate Human Oversight and Expertise
Expert Review: Have domain experts review AI-generated results to verify their accuracy and relevance. This is especially important for complex or high-stakes decisions.
Human-in-the-Loop Systems: Maintain mechanisms for human intervention where necessary, allowing experts to adjust or override AI decisions if they are misaligned with project goals.
6. Bias Detection and Mitigation
Assess for Bias: Continuously check the AI model for any biases in its outputs, especially if the model relies on data that could have inherent biases.
Diverse Training Data: Use diverse and representative datasets to minimize bias and ensure the model performs well across different scenarios.
7. Transparent Communication and Documentation
Document Assumptions: Keep a detailed record of all assumptions, data sources, and design decisions made during the development and implementation of the AI system.
Explainability: Prioritize models and techniques that provide explainable results, making it easier for stakeholders to understand and trust the outputs.
8. Regularly Update Models
Model Retraining: Schedule regular retraining of the AI models as new data becomes available or when there are significant changes in the environment or business needs.
Version Control: Keep track of different model versions and document updates to understand the impact of changes on performance.
9. Engage Stakeholders in the Evaluation Process
Feedback Loops: Involve stakeholders in reviewing and providing feedback on AI outputs. This ensures the system’s performance aligns with business requirements and user expectations.
Pilot Programs: Before full deployment, run pilot tests with stakeholders to gauge the AI model’s effectiveness and gather insights for refinement. Saving Changes...
I think you can build a test into the prompt to confirm it aligns with your goals in the same was a coder may utilize test-driven development (TDD). Saving Changes...
I don't have anything new to add to the great discussions above. I would say it's important to -
Be clear
Don't use jargon or specialized terminology
Provide the context for all of your requests
Be sure you provide the outcomes you are expecting
Iterate and refine
Saving Changes...
Anonymous
To get accurate and relevant results from AI, define clear goals, provide quality data, and regularly validate outputs. Stay involved to ensure AI supports your project effectively Saving Changes...
PMI Team Member
