As a data engineer, we finally reached decent tooling for the data stack, but now it feels like we are starting over with ad-hoc prompt pipelines. I built prompt-build-tool to bridge that gap.
The tool treats prompts as templatable code assets rather than static strings. It applies pipeline stability and data quality lessons from DE to the LLM lifecycle:
Declarative Pipelines: Build workflows by referencing outputs of previous prompts.
Granular Testing: Test prompts "segments" at the smallest level to quickly find regressions and understand system-level tradeoffs.
Scaling to Production: Manage complex meta-prompts and parallel execution paths while iterating quickly between dev and prod.
The tool treats prompts as templatable code assets rather than static strings. It applies pipeline stability and data quality lessons from DE to the LLM lifecycle:
Declarative Pipelines: Build workflows by referencing outputs of previous prompts.
Granular Testing: Test prompts "segments" at the smallest level to quickly find regressions and understand system-level tradeoffs.
Scaling to Production: Manage complex meta-prompts and parallel execution paths while iterating quickly between dev and prod.
https://github.com/conradbez/prompt-build-tool
Inspired by dbt (data-build-tool).