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?
Jacob K. N.Project Management ConsultantDallas, GA, United States
Two of the best practices for ensuring the results you receive are accurate, relevant, and aligned with your original goals are human in the loop approach and iterating prompt engineering. Saving Changes...
eduardo sanchezTecnical Solution Administrator| probiomedMexico City, Azcapotzalco, Mexico
Good questions Saving Changes...
PETER DATEMEArchitect| divineDimensions ConsultPort Harcourt, Rivers State, Rivers State, Nigeria
Where it is LLM based AI chatbot, I believe your prompt should be precise and concise with no ambiguity. Your prompt should be void of jargon and slang. this makes it easier for LLM understanding of your desired output.
You don't give room for AI to make assumptions and so your prompt should be specific enough. Saving Changes...
PETER DATEMEArchitect| divineDimensions ConsultPort Harcourt, Rivers State, Rivers State, Nigeria
Where it is LLM based AI chatbot, I believe your prompt should be precise and concise with no ambiguity. Your prompt should be void of jargon and slang. this makes it easier for LLM understanding of your desired output.
You don't give room for AI to make assumptions and so your prompt should be specific enough. Saving Changes...
Anonymous
Our instructions must be precise, we must provide relevant sufficient information, examples, success metrics, ask AI for references and sources, be iterative, proofread Saving Changes...
Don La FasoRevenue Management Specialist| HiltonFrisco, Tx, United States
Jun 08, 2024 1:37 PM
Replying to Keith Novak
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Like with any new tool, you need to test the results before you scale up.
Think about if you were to manually model a very complex problem in a spreadsheet. You don't build all the links and formulas first and then evaluate your final output. You build and test sections of the bigger solution first and then add on layers once you have validated the functionality.
hummmm...that sounds pretty efficient, concise and...agile...totally agree eventhough we do know sometimes things aren't always so simplistic. Saving Changes...
To obtain accurate and relevant information from a language model, it is essential to be specific in your inquiries. Clearly define your topic to guide the response effectively. Providing context enhances understanding and relevance. If the initial answer does not meet your expectations, do not hesitate to ask follow-up questions for clarification. Use straightforward language to facilitate clear communication. When seeking explanations, requesting examples can provide additional insight. If a particular format is needed for the information, specify this in your request. Regularly check for the latest updates, as information can change over time. These practices will improve the quality of the responses you receive. Ultimately, clarity in your requests leads to more effective LLM interactions. Saving Changes...
Farwa HassanProject ManagerToronto, ON, CAN, Canada
1- Use prompt formulas like CREATE, STAR, PEAR etc. to be more precise in your prompts.
2- Set specific goals or KPIs to evaluate the results.
3- Refine your prompts iteratively, giving feedback to the LLM.
4- Check the responses for accuracy requesting source citation, if applicable.
5- Beware of potential biases in AI algorithms and adjust as needed.
6- Collaborate with people to review and validate the recommendations.
7- Readjust according to the changes in circumstances. Saving Changes...
Harold Jose James PardoProcess excellence and Project Management Advisor| J Pardo ConsultantsAbu Dhabi, Abu Dhabi, United Arab Emirates
is back to the practice makes perfect routine. However, I have noticed that these LLMs "behave" differently; some allow files to be uploaded, some do not... some take specific guidelines well, others do not...
It is important to learn how the LLM takes the prompts and how they respond to them.
Bottom line: the better you know the LLM in question, the better responses you get, and significantly, I noticed that laziness in prompt writing leads to answers that are almost always useless and would need to be fully discarded and start from zero. Saving Changes...
Clarify Your Intentions: Before interacting with the AI, outline the specific goals and desired outcomes. This helps in framing queries effectively.
Be Specific: Provide as much context and detail as possible about what you need. Ambiguous or vague questions can lead to less useful results. Saving Changes...
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