Abstract: An enterprise Retrieval-Augmented Generation (“RAG”) pipeline data store may contain electronic records that represent RAG pipelines, each record including a pipeline identifier and at least one tuning parameter. A Generative Artificial Intelligence (“GenAI”) launchpad platform, associated with at least one Large Language Model (“LLM”), includes an enterprise RAG pipeline engine that accesses information associated with a first RAG pipeline that was created by a first tenant. The enterprise RAG pipeline engine receives from a user an adjustment to a tuning parameter of the first RAG pipeline (e.g., for document ingestion, chunk tuning, embed tuning, similarity search, and/or context tuning) and automatically calculates an overall pipeline credibility score for the adjusted first RAG pipeline. The pipeline engine may then display the overall pipeline credibility score to the user and store information about the adjusted first RAG pipeline into the RAG pipeline data store.