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?
Some of my items may be redundant but the most important things in my experience so far is:
Be precise and clear.
Be sure you explain jargon or specialized terminology
Provide the context for all of your requests
Be sure you provide the outcomes you are expecting
Experiment and refine as you go
I've found breaking down big problems can be better refined by chunking the whole into natural sections and working to refine each section and then working to put them back together.
Additionally, A key best practice when using AI systems is involving domain expertise—specialized knowledge in the relevant field—to ensure that the system’s outputs are accurate, meaningful, and applicable to real-world needs. Saving Changes...
When using AI systems is very hard to set the precision or accuracy of the responses. I love bringing in the Agile mindset here pretty much imagine if you are mentoring someone you do a Q&A and based on the reponses of your Mentee you give the feedback so that Mentee can align his/her thoughts in the direction that we hint similarly review the AI responses and using our rationale judgement
1- Give Feedback to the AI system
2- Rework on your promp and be specific on what is expected
3- Keep it short and conscise, guage the responses and slowly we can tune the AI system in a way to get the best output
4- Now the Tech. Solution that comes in for accuracy is havig specific set of APIs that talk to real and accurate data sources or use 2-3 outputs of LLMs and then analyze and bring the best in output.
, agile can make AI development faster and more responsive, but it requires special care—for example, keeping ethics, data quality, and thorough records a regular part of every sprint. Properly adapted, agile brings real benefits to AI work.
Agile sometimes neglects thorough documentation, which AI projects need for reproducibility and compliance.
If not careful, the rapid pace of agile could mean teams overlook transparency or ethical concerns with AI systems. Saving Changes...
Basically , its what goes in that determines what comes out . The more refined clear and concise your question / query so is the output Saving Changes...
Nilesh GhetiyaAssociated Director-Technical & Projects| Transit Electronics Ltd.Surat, Gujarat, India
Provide proper input, elaborate situation and format to get proper output Saving Changes...
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