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

span style="background-color: rgb(255, 255, 255); color: rgb(33, 33, 33);"When using AI systems, some of best practices for ensuring the results you receive are accurate, relevant, and aligned with your original goals are to be concise, give examples and verify results./span

You always have to clearly define the objectives by providing specific information for it to understand the overall goal. From that point you should simply perform some type of check and balance. If it doesn't seem accurate redefine the prompt until you can determine the context is accurate and what you requested. Ultimately, you own the project so you need to ensure the outputs align with the overall objectives of the project.

I agree with the discussion points above. I would approach this by designing a precise prompt, ensuring the underlying data is clean and verified, testing the initial outputs, and then refining the prompt as needed to confirm that the responses align with the project’s requirements. This iterative cycle is essential to achieving reliable, accurate results.

AI is such a broad term - I like to use different tools for different purposes and use outputs from one tool as an input for another. As an example, I do a lot of visual note taking in my work but sometimes I get stuck figuring out how best to represent something. In that case, I might use a LLM to help me write a better prompt for a generative AI tool to develop a graphic or illustration that accurately depicts or represents my points visually.

span style="background-color: rgb(255, 255, 255); color: rgb(33, 33, 33);"Some best practices for ensuring the results you receive are accurate, relevant, and aligned with your original goals when using Generative AI systems include starting with basics (keeping it simple), by ensuring clarity of the prompt, as well as building on the prompt (an iterative process). To that end, we must also ensure the accuracy of the data feeding into the prompt (garbage IN : Garbage OUT. Additionally, human review is essential in vetting the input and the analyses /span

Some best practices for ensuring the results you receive are accurate, relevant, and aligned with your original goals when using Generative AI systems include starting with basics (keeping it simple), by ensuring clarity of the prompt, as well as building on the prompt (an iterative process). To that end, we must also ensure the accuracy of the data feeding into the prompt (garbage IN : Garbage OUT. Additionally, human review is essential in vetting the input and the analyses generated from the prompt.

Here are some best practices for ensuring the results you receive are accurate, relevant, and aligned with your original goals/span

1. Apply structured format like CREATE for complete problems or RTF for simple business needs.
2. Ensure we feed in protected historical data for AI to understand the dynamic params.
3. Ensure quality gates/validation are in place. Validate with multiple AI systems before we publish results or act based on the outcomes from AI systems.