This approach demonstrates how engineers can rapidly build functional interfaces for complex APIs using LLMs and existing documentation, significantly reducing development overhead and improving accessibility for internal tools.
Turning OpenAPI spec and Markdown files into a conversational ads management tool — no compiled code required.
The post Building a Natural Language Interface to the Spotify Ads API with Claude Code Plugins appeared first on Spotify Engineering.
Continue reading on the original blog to support the author
Read full articleUsing LLMs as proxies in A/B testing can significantly reduce costs and time-to-market. However, understanding the theoretical limitations and necessary assumptions is crucial for maintaining experimental integrity and avoiding misleading results.
This article highlights how Spotify uses a context layer to bridge the gap between LLMs and complex internal data. It demonstrates a scalable way to encode domain expertise into AI assistants, significantly improving data discovery and reducing the manual burden on human experts.
LLM evals allow engineering teams to scale qualitative assessment, enabling faster experimentation and more reliable model deployment by replacing or augmenting slow human review with automated, consistent judging.
This demonstrates how to turn massive datasets into personalized user experiences at scale, a key challenge for data-intensive consumer applications.
