Series
Every module up to this point used the same streaming events without ever reading two fields that were there the entire time. message_start and message_delta carry token usage on every single stream —
Everything in the earlier modules assumed the happy path: the API responds, the model generates valid output, tools return results, the stream closes cleanly. In production, none of that is guaranteed
04-structured-output solved the reliability problem: use forced tool use to guarantee valid JSON back from the model. But it waited for the complete response before doing anything with it. For a backe
The first time I ran evals on my LLM playground, the agent scored lower than basic RAG — 7.5 vs 8.9. The agent is more capable: it has live tool data on top of retrieval, it can answer questions basic
In the basic RAG module, retrieval was unconditional. Every query triggered the same pipeline: embed the query, search the vector store, stuff the top chunks into the prompt, generate. The model had n
Most LLM features eventually need the model to return structured data like a JSON object your application can parse and act on. Connecting LLM output to a database, a UI component, or a downstream API
