Okay, hear me out. I've been knee-deep in trying to automate scoring for our support bot's responses using a couple of the popular eval-as-a-service platforms. They're great for generic stuff like "helpfulness" or "relevance," but when I needed to check if the bot correctly parsed a user's *specific* request for a **feature tier change** and referenced the **correct SLA window**... the generic tools fell flat.
Their pre-built metrics couldn't grasp our internal business logic. I ended up building a custom evaluator using the LLM's own API, and it's been a game-changer for precision.
Here's the core pattern I landed on:
- I feed the LLM (like GPT-4 or Claude) the original user query, the bot's response, and a custom scoring rubric **written in our domain language**.
- The rubric asks very specific yes/no questions based on our business rules.
- The evaluator LLM returns a structured JSON scorecard.
A simplified version of my prompt structure looks like this:
```json
{
"eval_rules": [
"Does the response confirm the feature tier mentioned by the user?",
"Does the response correctly state the 48-hour processing SLA for tier changes?",
"Does the response avoid promising immediate escalation (against policy)?"
],
"output_format": {
"score": "integer from 0 to 3",
"reasoning": "string",
"rule_breakdown": "object mapping each rule to boolean"
}
}
```
I then run this in a Make scenario for each test case, logging the results to Airtable. The beauty is:
* The rubric evolves with our product rules.
* I can weight certain rules more heavily.
* It's dirt cheap compared to some enterprise eval tools, and it *understands* our context.
Has anyone else gone down this path? I'm curious how you're handling the orchestration—especially if you're testing hundreds of examples. Do you batch them? Use a dedicated model for evaluation?
So you traded one opaque vendor platform for another, just with a more flexible pricing page. Sure, the generic tools fell flat, but now your "precision" is entirely dependent on the LLM vendor's whims regarding output structure, pricing, and API stability.
That custom rubric is just another prompt. Are you validating its consistency? What's your drift rate when the vendor tweaks their model? You've swapped pre-packaged lock-in for a more bespoke, and potentially more expensive, dependency.
If it's free, you're the product. If it's expensive, you're still the product.