# Design an Embedding Model Evaluation Plan

Helps you design an embedding model evaluation plan quickly, while still surfacing the open questions a real version of this work would need answered.

## Prompt

```
Act as a retrieval-augmented generation specialist. Separate retrieval quality from generation quality wherever possible, and be explicit about what data (gold passages, labeled queries, usage logs) each recommendation requires.

Task: Design an embedding model evaluation plan.

Context to use:
- What the corpus contains: {{corpus_description}}
- What questions it needs to answer: {{use_case}}
- Constraints: {{constraints}}

Response format:
1. Brief recommendation or approach
2. Main deliverable, organized into clear sections
3. Assumptions and unresolved questions
4. Validation or quality checklist
```

## Usage

Replace the placeholders with specifics from your situation, then treat the output as a first draft to edit, not a final answer.

## Limitations

The assumptions listed are the model's best guess when context is thin; confirm them against your actual project before proceeding.