When using AI systems, what are some best practices for ensuring the results you receive are accurate, relevant, and aligned with your original goals?

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.

A very good question and also difficult to answer as well. However you have to go to the basics and say as far as you are concerned, how well are you versed with the subject at hand ?. There are facts which the AI will generate and if you can verify these facts the more reliable the generated response will be. The fewer the facts then it means that the Generative AI response is far from meeting your original goals. Then it becomes very critical that you review the accuracy , relevancy and the alignment of the response to your original need. Unfortunately there are no clearly defined metrics that one can use a model to evaluate an AI generated response. So from my personal experience I basically restrict AI to an area where i have sound knowledge of , else it becomes almost impossible to verify details generated by an AI if you venture into unchartered territory. However with long usage and exposure your confidence also tend to increase as well.

The best practice and protocol to follow would be to consult subject matter expects to validate the AI generated response before making critical decisions based on it to avoid any inherent associated risks which you might be not aware of.

1. As a Project Manager, we should draft a detailed prompt using one methodology to achieve our project objectives.

2. When we review the results, we should carefully examine the outputs and note any further refinements needed in the prompt.

3. We should also adopt the perspective of a Project Sponsor and evaluate the output from that viewpoint, as more details may be required.

4. Based on the observations from points 2 and 3, we can modify the prompt, which may help us achieve the desired output with the necessary accuracy.

5. As Project Managers, we should maintain a clear overview of what the desired outcomes should be. Since AI is a vast field with endless possibilities for accuracy, it's important to have a solid blueprint in mind. We should use AI to fine-tune our efforts to ensure the success of the project.

6. As we gain experience using AI, we should learn to draw a line; otherwise, the process can feel never ending.

I just go back and forth until I get the answer that I think I am looking for.

I just go back and forth until I get the answer that I think I am looking for.