Generative AI is transforming how we build applications, but the inherent unpredictability of AI outputs often creates a critical challenge: ensuring data integrity. Receiving malformed or unexpected data can lead to frustrating runtime errors and compromise the reliability of your application. This is where Functions.do shines, offering a robust platform for executing AI functions that prioritize typesafety and validation, ensuring your applications "just work."
When you integrate Generative AI into your workflow, you're relying on a model to generate data based on your prompts. However, AI models can sometimes produce outputs that don't perfectly align with the expected structure or data types. This can happen due to various reasons, including subtle variations in prompts, model updates, or the inherent probabilistic nature of AI.
Without proper validation, your application might receive data that:
Processing this invalid data can lead to unexpected behavior, crashes, and a poor user experience. Debugging these issues, especially in a complex application with multiple AI integrations, can be time-consuming and resource-intensive.
Functions.do is built with reliability in mind. It tackles the challenge of unpredictable AI outputs head-on by leveraging compile-time type checking and runtime validation.
Here's how Functions.do empowers you to build reliable applications:
Functions.do allows you to define the expected output shape of your AI functions using types. This isn't just a suggestion; it's a contract. When you define a function that interacts with an AI model, you specify the data structure and types you anticipate in the response.
import { AI } from 'functions.do'
const ai = AI({
leanCanvas: {
productName: 'name of the product or service',
problem: ['top 3 problems the product solves'],
solution: ['top 3 solutions the product offers'],
uniqueValueProposition: 'clear message that states the benefit of your product',
unfairAdvantage: 'something that cannot be easily copied or bought',
customerSegments: ['list of target customer segments'],
keyMetrics: ['list of key numbers that tell you how your business is doing'],
channels: ['path to customers'],
costStructure: ['list of operational costs'],
revenueStreams: ['list of revenue sources'],
recommendations: ['list of recommendations based on the analysis'],
},
})
In this example, we've clearly defined the expected structure for the leanCanvas output. Functions.do uses this information to perform compile-time checks. This means potential type mismatches or structural issues can be identified before your code is even executed, catching errors early in the development process.
Even with compile-time checks, AI models can still produce unexpected outputs. Functions.do goes a step further by implementing runtime validation. When the AI generates a response, Functions.do automatically validates that response against the defined type.
If the AI-generated data doesn't match the expected type or structure, Functions.do will flag it, preventing malformed data from propagating through your application. This proactive validation dramatically reduces the likelihood of runtime errors caused by unexpected AI outputs.
By catching validation errors early, Functions.do simplifies your error handling logic. You're not left guessing why your application crashed; you receive clear feedback when an AI response fails validation. This allows you to implement specific error handling strategies, such as retrying the AI call, providing default values, or alerting the user.
Functions.do doesn't just make AI integrations more reliable; it simplifies the entire process of building Services-as-Software and implementing Business-as-Code. By providing a typesafe platform for defining and executing functions, including those powered by AI, Functions.do allows you to focus on the business logic rather than wrestling with complex infrastructure or unpredictable external dependencies.
Whether you're building an API, an internal service, or a user-facing application, Functions.do provides the foundation for creating robust, reliable, and maintainable software that leverages the power of Generative AI without the associated complexity.
Ensuring data integrity is paramount when building applications that rely on Generative AI. Functions.do provides a powerful solution by combining typesafe function execution with robust runtime validation. By catching potential issues at compile time and validating AI outputs at runtime, Functions.do empowers developers to build reliable applications that "just work," freeing them to focus on innovation and delivering value.
Ready to build Generative AI functions with confidence? Explore Functions.do and experience the power of typesafe function execution.