LLM Evaluation Services

The problem

“The model looks good” is not evaluation. A demo with three happy-path prompts tells you nothing about instruction following on messy inputs, JSON that must parse, or whether last week’s prompt change quietly made answers longer, ruder, or wrong.

LLM evaluation is the discipline of defining what good looks like for your use case, then measuring outputs against that definition across versions.

This is not AI agent testing (tools and actions) and not RAG evaluation (retrieval). Those pages assume extra machinery. This page is the model-in-the-product: prompt in, text (or structured data) out.

What’s at risk without it

• Shipping a prompt change because it “felt better” on five examples
• Structured outputs that break the next service in the chain
• Tone or policy drift after a silent provider update
• No way to compare model A vs model B on your tasks
• Arguments in Slack instead of a recorded score

What we evaluate

• Instruction and system-prompt adherence
• Output format (JSON, citations, length, required fields)
• Task completeness on a fixed scenario set
• Factual checks where a source of truth exists
• Refusal and hedging behavior when the question is out of scope
• Version diffs: prompt, model, temperature, or provider

We do not sell a public LLM leaderboard. If you need hallucination testing as the main risk, start there. If you need safety bypasses, that is AI red teaming.

How LLM evaluation runs

1. Write the rubric. What must be true for an answer to pass in this product — not a generic “helpfulness” score.
2. Freeze a set. Real tasks from your product, plus edge and adversarial prompts.
3. Score. Automated checks where the rule is mechanical; human review where taste or domain judgment matters. LLM-as-a-judge is a tool, not a religion — we say when it is the wrong judge. Metrics we actually use are on LLM evaluation metrics.
4. Compare versions. Same set, new prompt or model, a diff you can read.
5. Leave the set behind. So the next change is cheaper than the first.

Parent workstream: AI testing services. Engagement: QA outsourcing or QA as a service around a model swap.

Deliverables

• A written rubric and scenario set for your use case
• Scores and qualitative notes per version
• A short recommendation: ship, fix the prompt, or do not switch models yet

Suitable for

Teams adding an LLM to search, support, drafting, extraction, or classification who need to compare changes without trusting a vendor blog.

FAQs

Is this the same as “LLM as a judge”?

LLM-as-judge can be one scoring method. Evaluation is the rubric, the set, and the decision. We will not use a judge model as the only signal when the task is factual or safety-critical.

Do you evaluate open-source and API models?

We evaluate whatever you call from the product. The work is the same: your tasks, your definition of correct.

Can you tell us which model to buy?

We can tell you which of the versions you ran did better on your set. We do not run a vendor bake-off as a standalone research product.