"Tightness" in Real-World AI Applications

Tightness is a critical factor in making AI work well in business. When models are tight, they become more efficient, reliable, and valuable, directly contributing to better outcomes across different industries.

Let us look at a few examples of practical AI applications that are being built or can be built within a business. While we have to look at tightness from a pithiness perspective sometimes we have to weigh the measure from the domain context.

• Customer Support Automation: AI chatbots are becoming the go-to for handling customer questions and problems. For them to work well, they need to be "tight" — giving clear, relevant answers without getting stuck in loops or providing useless info. Imagine a bot that just keeps repeating itself or asks unrelated questions; that's frustrating and wastes everyone's time. A tight chatbot wraps things up quickly and accurately, keeping customers happy and saving time.

A challenge here may be how do we capture the ignorance of the AI chatbot. Can it understand its limitations and provide an answer that it doesn't know for cases where it is not sure so that the model is not only tight, but easy to test and improves reliability.

• Financial Reporting and Forecasting: In finance, AI is used for reports, trend analysis, and predicting performance. The key is for these AI models to be "tight" — giving focused, clear forecasts without going off on tangents or providing mixed signals. If the AI spits out endless, conflicting predictions, it confuses decision-makers. A tight model keeps things clear and helps make quick, smart decisions.

A challenge here is if there are conflicting predictions which in some domains may be natural. How then can we look at the AI model to provide a balanced view of the conflicting predictions with different underlying assumptions. How do we enable the AI model to reflect on its assumptions while providing a prediction so that tightness can be preserved but different perspectives can be reviewed.

• Marketing Content Creation: AI tools are great and very useful for creating content like blog posts, social media, or emails. Tightness is vital here to keep the message engaging and to the point. Imagine an AI writing a marketing email that rambles on without getting to the point; people would stop reading. A tight model makes sure the content hits all the right notes, keeping the audience interested and boosting campaign effectiveness.

Challenge here is that we are not looking to create a short post/email but an effective post / email. So representing tightness from a relevance and the emotional appeal it creates in a human or a set of humans might be something one needs to evaluate creatively.

• Healthcare Diagnostic Tools: AI tools may be able to help doctors diagnose by analyzing data and suggesting possibilities. Tightness here means the AI gives a useful list of likely diagnoses, not an overwhelming, endless array of conditions. If a tool isn't tight, it might flood doctors with too many options, complicating their decisions. A tight model ensures fast, accurate diagnoses, improving patient care.

Again there is a tight balance here because a rare condition might be ignored if the tool is too tight. So a doctor might need a response that is tight but also have a less tight response when the evidence is confounding.

• Legal Document Drafting: Law firms may use AI to draft contracts and review documents. A tight language model ensures the documents are clear, complete, and legally solid, avoiding confusion or unnecessary text that could lead to disputes. If the AI isn't tight, it might create long-winded contracts with irrelevant details, increasing the risk of mistakes. Tightness helps produce concise, precise documents faster and cheaper.

Here again the relevance is more contextual and complex contracts have to take into account several scenarios, so making sure that most of these scenarios are articulated in the knowledge base of the AI model may be important to create strong contracts.

• E-commerce Personalization: E-commerce platforms are starting to use AI to suggest products based on customer behavior. Tightness ensures these recommendations are relevant and useful. Without tightness, the AI might endlessly suggest unrelated items, annoying customers. A tight model focuses on what the customer actually wants, improving the shopping experience and boosting sales.

We need to make sure that some amount of serendipity is accounted for in the model to capture the ignorance of the collective wisdom of the crowds may be the relevance should be weighted by a smaller sub-crowd wisdom rather than the wisdom of the entire crowd.

Conclusion: As we build AI applications we need theoretical measures but also innovative techniques to operationalize these measures for different domains. Follow me for more articles on building AI applications for streamlining business workflows.