Four ways to optimize your AI feature post launch

Like any release, shipping an AI feature to production is only the starting point. Unlike the rest of your tech stack, LLMs introduce a non-deterministic element to your product. To maintain consistency, you need a data-driven evaluation system in place.

In this week’s article, we share examples of how to analyze your AI features, make pre-trained models more specific, and leverage LLM data effectively.

Topics covered in this article:

• Analyze usage, performance, and costs of your AI

• Evaluate effectiveness of prompt engineering

• Prepare your data for fine-tuning models

• Forward logs to any platform

Articles we enjoyed this week:

The launch of Figma AI: As power users of Figma, we’re excited to use new AI features that automate time-consuming and tedious tasks. Like Apple’s AI announcement, these features illustrate AI capabilities that go far beyond chat.

Comparing fine-tuned models to OpenAI: An in-depth overview of how fine-tuned Mistral, Llama3, and Solar LLMs compare to OpenAI performance. Also worth a scan of the HN comments debating the topic.