Building Scalable AI Workflows with Fastnotry

Introduction to Scalable AI Workflows

As AI adoption grows within organizations, the need for scalable, maintainable workflows becomes critical. In this post, we'll explore best practices for building production-ready AI workflows using Fastnotry.

The Challenge of Scale

Many teams start with simple, one-off AI implementations. However, as usage grows, they often face:

Designing for Scale

Here's our recommended approach for building scalable AI workflows:

1. Centralize Your Prompts

Store all prompts in a central repository. This ensures consistency and makes it easy to update prompts across all applications.

import { Fastnotry } from '@fastnotry/sdk';

const client = new Fastnotry({

apiKey: 'your-api-key-here',

});

const prompt = await client.prompts.get('customer-support-v2');

2. Implement Version Control

Track changes to your prompts over time. This allows you to:

3. Monitor and Measure

Set up monitoring for key metrics:

4. Implement Caching

Cache frequently used responses to reduce costs and improve latency:

const response = await client.execute({

promptId: 'product-description',

variables: { productName: 'Widget Pro' },

cache: {

enabled: true,

ttl: 3600, // 1 hour

},

});

Real-World Example

Let's look at how a customer support team might implement a scalable workflow:

1. **Intake**: Customer messages are received via API

2. **Classification**: Fastnotry classifies the intent

3. **Routing**: Messages are routed to appropriate handlers

4. **Response**: AI generates contextual responses

5. **Review**: Human review for edge cases

This workflow handles over 10,000 requests per hour while maintaining 98% accuracy.

Conclusion

Building scalable AI workflows requires careful planning and the right tools. Fastnotry provides the infrastructure you need to move from experimentation to production with confidence.