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I agrees with the answers above. When using AI is very difficult to ensure the results you receive are accurate, relevant and aligned unless the prompt is very clear/ specific.
INDUMATHI KANNAYIRAM
PROJECT MANAGER| DELTASTAR POWER PROJECTS SERVICES LLC
Abudhabi, U.A.E, United Arab Emirates
While Providing Prompts we need to use the
RTF formula for simpler tasks and CREATE formula for Complex Scenarios are some best practices for ensuring the results are accurate, relevant, and aligned with your original goals? 
The practice will give us the expertise to improve our prompts. However, it´s very important to check and validate the returned information.
Validating AI responses starts with the design of the prompts. Prompts should be written clearly with the relevant persona, task and contextual information, examples and additional constraints included. Where assumptions are being made, ask AI to state those clearly and provide it with templates or guidelines for the desired output. It is important to ensure that sufficient information is given and the prompt is refined as much as possible, including feedback to previous outputs, to produce the most accurate and satisfactory answer,
Use the prompt pattern that is most appropriate for your objective.
Be concise. Ask for references. Confirm Details. 
Ravinder Kaur Ahluwalia
Project Manager, SaFe and Agile Coach| Asoft Management Consulting Inc
Woodbridge, Ontario, Canada
I agree with the discussions.
Cornelius Chamia Mutuku
Athi River, 22, Kenya
To ensure that AI results are accurate, relevant, and aligned with your goals it is important to ensure that the prompts used are specific and the interaction is structured. Start by providing clear context then apply your prompts systematically by interacting with the feedback and guiding the AI to more refined results at each step until the outcome meets your requirements.
Doris Cobb
Kissimmee, Fl, USA
In LLMs, RTF and CREATE is a great way to initiate detailed responses. However, working with prompt engineering, methods with the correct verbiage will yield quality-valued, results.
it is important to have validation systems made up of subject matter experts who know what good should look like. There are also automated systems that fact check the output of AI these days. Everything will depend on the context and what the goals are. Frequent feedback to the AI will increase the accuracy of the LLM.
When using AI systems, it's crucial to follow some best practices to ensure the results are accurate, relevant, and aligned with your goals. Start by clearly defining your objectives and the specific outcomes you want from the AI system. High-quality, relevant data is essential for training and validation to achieve reliable results. Regularly update your AI model with new data to keep it accurate and effective. Be mindful of potential biases in your data and implement strategies to minimize them. Consistently validate the AI outputs against known benchmarks to ensure they meet your standards. Human oversight is important to review and interpret AI results, providing an additional layer of accuracy. Ensure transparency in the AI’s decision-making process so it's understandable and trustworthy. Finally, establish a feedback loop to continually improve the AI system based on user input and changing requirements. |
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