Are you exploring the options to make your Generative AI solution better than what exists in the market?
Generative AI has seen an upsurge in usage in recent times, enabling the production of diverse content types like text, images, and recommendations. Nevertheless, the “rule of 3,” a straightforward and practical approach, can be utilized to ensure that the results produced by these systems align with the intended specifications and are pertinent to the task at hand.
The rule of 3 involves identifying three critical factors that are relevant to the success of the generative AI system. These factors vary depending on the specific application, and careful consideration is necessary to ensure they are meaningful and relevant to the problem being solved.
By training the generative AI model to consider these three factors, the output generated is tailored to the specific needs of the task at hand. For instance, when generating text, the three key factors might be the topic, tone, and intended audience. By training the generative AI model to consider these factors, the output generated is relevant to the topic, uses appropriate language, and is tailored to the audience’s preferences.
Similarly, when generating images, the three key factors might be the subject, style, and intended use, ensuring output relevance and suitability.
Behavior, preferences and options
In the context of providing recommendations, the three key factors might be the user’s past behavior, current preferences, and available options.
By training the generative AI model to consider these factors, the recommendations provided are tailored to the user’s interests and past behavior, providing relevant options based on these factors. The rule of 3 is not only about identifying key factors.
It also involves a process of iterative refinement in which the output generated by the generative AI model is evaluated against the three key factors, and adjustments are made as necessary.
This process may involve adjusting the training dataset, refining the model architecture, or making other changes to ensure that the output generated meets the desired criteria. In practice, the rule of 3 can be applied to various applications, such as marketing, content creation, and recommendation systems.
By training generative AI models to consider the critical factors identified through the rule of 3, businesses can improve the relevance and effectiveness of their content and recommendations, ultimately leading to improved customer satisfaction and engagement.
Furthermore, the rule of 3 can be particularly useful when working with large datasets and complex models. In such cases, the rule of 3 allows businesses to focus on the critical factors that are most relevant to their specific needs, enabling them to create more effective and relevant content and recommendations.
Applying the “rule of 3” to generative AI systems can be challenging due to several factors. Firstly, determining the appropriate criteria to evaluate the quality and relevance of the output can be difficult and needs to be aligned with the specific use case. Secondly, setting threshold values for each criterion requires striking a balance between leniency and strictness.
Moreover, some criteria may be subjective, such as artistic value, which can make it challenging to maintain consistency in the evaluation process. Additionally, adapting the rule to different domains and keeping up with evolving technology can pose challenges, as what works well in one domain may not be suitable for another, and the criteria may need to be revised with the changing landscape.
The rule of 3 is a simple yet effective technique that can be applied to generative AI to provide better contextualization. By identifying three critical factors that are relevant to the success of the generative AI system and training the model to consider these factors, businesses can generate output that meets their specific needs and improves customer satisfaction and engagement.
The iterative refinement process involved in the rule of 3 ensures that the output generated is continually evaluated and adjusted as necessary, resulting in more effective and relevant content and recommendations.
Muthu Chandra is the Chief Data Scientist at Ascendion where he leads a team of data scientists and engineers in building and deploying machine learning models to solve real-world problems.
He holds a master’s in technology from Birla Institute of Technology and Science, Pilani, and has written articles about generative AI contextualization, exploratory data analysis , and data fingerprinting, among other topics. Muthu is passionate about the journey to discover the next groundbreaking AI innovation that can revolutionize lives.