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?
In addition to the afore mentioned, to ensure that the results you receive from AI systems are accurate, relevant, and aligned with your original goals, do the following:
Be very specific about what you are asking for and include all important parameters like subject matter or knowledge area, the level of details required, the required tone, time frames, locations, output format, technical information, etc. in your prompts. Verify the output and refine your prompt as needed until you obtain your desired response. Saving Changes...
To ensure AI outputs are accurate, relevant, and aligned with original goals, it's crucial to define clear objectives, refine prompts iteratively, and implement strong testing protocols that compare results against benchmarks or expert evaluations. Cross-checking outputs with external data sources or human feedback helps validate factual accuracy, while establishing quality criteria, such as consistency and coherence, ensures outputs meet desired standards. Continuous monitoring and refinement further enhance the reliability and relevance of AI-driven results.
Best regards. Saving Changes...
Isaac MartinezProgram Support Specialist| RaytheonWorcester, MA, United States
In my experience, the best results include...
• a clearly defined goal/outcome
• appropriate level of context
• mindful evaluation of Ai output
• conscientious effort to improve feedback loop Saving Changes...
What strategies have you found most effective in validating AI-generated outputs to ensure they meet your goals and expectations, and how do you continuously refine your approach to maintain accuracy and relevance? Saving Changes...
Validating outputs and checking sources are key! It's also always great to cross reference with how your specific company or team does business. Saving Changes...
I agree that with clear prompts using RTF / CREATE formula, you will get a close to accurate responses from AI. But it is always better to validate it with the subject matter experts when you are new to the topic of discussion.
Here is my personal experience. I had used AI tools for developing some excel macros to create some reports. Believe me, when I gave very clear and detailed inputs with examples with the expected output format, the macro generated by AI exactly worked like a charm. I got what I wanted. But still there were situations where the program did not run at the first hit. I continue to feedback with the exact error messages and it regenerates the macro function. I do this till the program runs perfectly with the expected report created. Since I am technical, apart from the error message, I also share the flaws in the logic it used as part of feedback that helped to get the right response.
Hence I feel that it is better to get the responses validated with SME or asking for justification on the response again with AI to ensure it is more appropriate. Saving Changes...
Anonymous
For ensuring accurate results - prompt the AI to use referencable sources.
For ensuring relevant results - calibrate the response using a persona.
To ensure alignment with one's goals, upload or supply these goals to the AI and prompt the AI to align the response with them. Saving Changes...
Jose InsenserServices Portfolio Manager| AirbusMadrid, Spain
Clearly defined objectives, accurate data, and customized models ensure AI achieves intended goals effectively while avoiding unintended consequences. Rigorous testing in iterative feedback cycles validate robust, seamless integration into workflows with human guidance. Governance establishes accountability and compliance with ethics rules and laws. Prioritizing high-value uses of shared infrastructure optimizes resources across departments. Educating stakeholders on capabilities and limitations promotes appropriate expectations and an adaptable, goal-focused evolution.
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