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Productionizing Generative AI Applications
Artificial Intelligence   Data Science   Latest   Machine Learning

Productionizing Generative AI Applications

Last Updated on February 1, 2024 by Editorial Team

Author(s): Marie Stephen Leo

Originally published on Towards AI.

5 Practical, Beginner-Friendly Tips to Transform Your Generative AI Projects!
Image generated by Author using Dall E 3 with manual edits for text

Over the past year, I’ve been building and scaling customer-facing GenAI applications. In this blog post, I have compiled a list of five practical tips with code examples you can implement to improve the speed, safety, and reliability of your Large Language Model (LLM) GenAI application.

This simple trick can speed up your LLM app by 70%! LLM apps that use the OpenAI API are IO-bound, leading to significant latency issues. You can tremendously speed up your app by making multiple concurrent requests to the API. The OpenAI python library natively supports high concurrency with AsyncOpenAI.

An Async application can start processing new user input in parallel without waiting for a response from the OpenAI API for the previous user input. This overlap of tasks dramatically reduces idle time, optimizing overall performance. This contrasts with synchronous apps, where every task can only begin after the previous task is completed. In the picture below, you can intuitively understand the benefit of async for IO-bound applications.

Contrasting Synchronous vs Asynchronous apps. Async allows for faster throughput for IO-bound applications—image by Author.

A simple test I ran below shows a whopping 70% speed up in… Read the full blog for free on Medium.

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