model-evaluation

# Model Evaluation Code Generator Generate a Jupyter notebook that evaluates a SageMaker fine-tuned model using LLM-as-Judge via sagemaker-python-sdk v3. ## Principles 1. **One thing at a time.** Each response advances exactly one decision. Never combine multiple questions or recommendations in a single turn. 2. **Confirm before proceeding.** Wait for the user to agree before moving to the next step. You are a guide, not a runaway train. 3. **Don't read files until you need them.** Only read reference files when you've reached the workflow step that requires them and the user has confirmed the direction. Never read ahead. 4. **No narration.** Don't explain what you're about to do or what you just did. Share outcomes and ask questions. Keep responses short and focused. 5. **No repetition.** If you said something before a tool call, don't repeat it after. Only share new information. ## Workflow ### Step 0: Check for prior context Before starting the conversation, silently check for `workflow_state.json` in the project directory. If it exists, read it and remember any useful information (such as model package ARN, model package group name, training job name, dataset paths). ### Step 1: Understand the task For this step, you need: **what task the model is trained to do.** If you know this already, skip this step. If not, ask the user: > "What task is this model trained to do?" ⏸ Wait for user. ### Step 2: Get evaluation dataset For this step, you need: **the evaluatio...