I created a simple Node/Express microservice that generates structured ecommerce copy based on Zod schemas, following an OpenAPI 3.0 specification. The service validates input, ensures the output is structured JSON, and logs usage. It's used for generating ecommerce listing packs from product features, but my main question is about handling LLM-backed endpoints. For those who've implemented such services, what are your strategies for keeping responses consistent and manageable over time? Any tips on schema enforcement, caching, fallback models, or potential pitfalls to watch out for when integrating this into bulk catalog workflows?
3 Answers
It's great that you're logging request metadata! I recommend keeping track of the usage patterns and response times, as that can help in tuning your model. Also, you might consider setting up evaluation tests to compare outputs against expected structures. This can help with maintaining reliability over time. Just make sure that the AI parts are transparent, so users understand the tech behind it, even if it’s well-structured output!
Honestly, I went for a simple API without any complex wrappers. Just keep it straightforward like you’re doing! If you find a lot of interest later, you can always introduce more features like an MCP. Focus on building a solid foundation first, and the rest can follow based on user feedback.
Exactly! It's better to start simple and perfect your core workflow first.
For my LLM API, I ensure that all responses are validated against strict schemas before they're sent out. This helps avoid that dreaded 'AI mush.' I’d recommend implementing fallback models as a backup to help maintain quality, especially during high traffic. Just be aware that well-structured schemas can get complicated if they’re too restrictive, which might lead to failure in edge cases.
I can see how that might be tricky! Have you had any issues with the schemas being too strict?
Are you also doing any logging for the actual outputs? That can be super useful for debugging later!