Executable tutorial proposal: LLMOps prompt regression testing

contributions/executable-tutorial/jiaxun-jingze/README.md

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+# Assignment Proposal
+
+## Title
+
+Prompt regression testing for LLM applications (LLMOps)
+
+## Names and KTH ID
+
+  - Jiaxun Wei (jiaxun@kth.se)
+  - Jingze Guo (jingze@kth.se)
+
+## Deadline
+
+- Task 2
+
+## Category
+
+- Executable tutorial
+
+## Description
+
+We want to write a Google Colab notebook that shows how to keep an LLM application stable when its prompt or its model changes. The reader writes a promptfoo configuration with test cases and assertions, runs it against a small instruction-tuned model that is loaded locally in the notebook. The reader then breaks the prompt on purpose, watches the assertions fail, and finally wires the evaluation into a pipeline, so that a failed evaluation blocks the change.
+
+**Relevance**
+
+An LLM application is defined as much by its prompts and its model version as by its code, and both change often. Without evaluation, a reworded prompt or a model upgrade can silently degrade behaviour, which is the LLMOps version of a regression. The tutorial makes that behaviour testable and automated, so that changes to a model-driven system get the same review discipline as changes to code.