# Motivation

## Problem

Unlike traditional software applications, AI applications exhibit **continuously evolving behavior** due to factors such as **updates to language models, shifts in context, data drift, and changes in prompt engineering or system integrations**. Changing behavior is of great concern to almost any organization building these applications, and is something that they **constantly need to address**.

If not addressed it is hard for organizations to:

#### **Defining Desired Behavior**

Organizations struggle to define what "good" looks like for their AI applications. Current metrics fail to capture the full scope of desired behavior because:

* **Context Matters** – AI behavior differs across applications, use cases, and environments.
* **Metrics Have Limits** – No single set of measurements can fully capture performance across all dimensions.
* **Evolving Requirements** – Business needs, regulations, and user expectations shift over time, requiring continuous refinement.

#### Understanding and Addressing Changes

When applications changes from expected behavior, organizations struggle to:

* **Pinpointing Root Causes** – Identifying why behavior changed can be complex.
* **Adapting vs. Fixing** – Deciding whether to refine behavior definitions or modify the application itself.
* **Prioritizing Solutions** – Knowing where to start when addressing issues.
* **Ensuring Effectiveness** – Validating that changes lead to meaningful improvements.

The uncertainty of AI applications often prevents AI applications from seeing the light of day. For those that reach production, teams lack the tools to effectively monitor and maintain their performance.

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## Solution

Distributional helps organizations **understand and track** how your AI application behaves in two main ways:

First, it watches your application's **inputs and outputs over time**, building a picture of “expected” behavior. Think of it like your **AI applications fingerprint** - when your applications fingerprint changes Distributional notices and alerts you. If something goes wrong, it shows you exactly what changed and **helps you figure out why**, making it easier to fix problems quickly.

Secondly, the system **keeps track of everything** it learns about your application's behavior, saving these insights for future use. Organizations can apply what they **learn from one AI application** to similar ones, helping them test and improve **new applications more quickly**. This creates a consistent way to **test AI applications** across an organization and ensures that your AI application reaches maximum up-time.


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