Question

A Generative AI Engineer builds a LangChain chain on Databricks that extracts contact details (name, email, phone) from customer inquiry emails. The LLM is instructed to return the details, and the extracted values must be passed to a downstream Python function that expects a validated dictionary with those three keys. Currently the chain returns raw text from the model, and the downstream function frequently fails when the model adds explanatory prose or inconsistent formatting. Which change most directly addresses this problem within the LangChain chain?

Accepted Answer

Append a Pydantic-based structured output parser (e.g., with_structured_output or a PydanticOutputParser) so the model response is coerced and validated into the expected schema

Answer

Add a StrOutputParser to the end of the chain so the raw model string is cleanly extracted before the function call

Answer

Increase the temperature of the foundation model so it produces more varied phrasings of the contact details

Answer

Wrap the LLM call in a RunnableParallel so the contact fields are generated concurrently and merged