“Evals” are tests for your AI.
So what are we doing with evals? Well, LLMs are famously stochastic. We have an idea of what they might generate given a specific input, but we don’t know. That’s okay though, we can manage and mitigate that risk of not knowing even in production.
BUT, we do need a means to judge that risk. And that’s where our evals come in. Usually, an eval framework gives you the capability to run a multitude of AI Scenarios, gather all the results, and compare those results to the preferred results. This allows us to start building towards a confidence level in the AI workflow.
Example Eval: "Support-Email Triage"
Say you’re building an LLM workflow that processes an incoming customer support email and outputs:
- A category (Billing / Bug / Feature Request / Account / Other)
- A priority (P0–P3)
- A one-paragraph draft reply that follows policy (no refunds promised, no private data echoed, etc.)
What Goes Into an Eval Set
Your eval set might be 50–200 real (anonymized) emails, each with a preferred outcome. For each test case, you store:
Input: The email text
- Expected outputs: Category, priority, and a rubric for the reply
- Scoring Rubric: Deterministic processes or context for anllm-as-judge scorer
Sample Test Case
One test case might look like:
Input email: “I was charged twice for my subscription this month. What the heck? My card ends in 4432.”
Expected category: Billing
Expected priority: P1
Reply rubric (graded):
✅ Acknowledges the double charge and apologizes
- ✅ Says they can help investigate/refund if confirmed (does not promise a refund unconditionally)
- ✅ Requests safe identifiers (invoice ID / date), and does not repeat the card digits
- ✅ Provides next steps and expected timeline
What an Eval Run Produces (The Proof, Not The Vibes)
- Accuracy: Category correct in x% of cases; priority correct in y%
- Safety/compliance: “No payment details echoed” passes x% (1 failure is a blocker)
- Quality score: Total rubric score x out of y
Now you’re not saying “it feels like the assistant is doing okay”—you can point to measurable performance.
The Real Goal: Production-Level Confidence
Confidence in our workflow, production-level confidence, is the major use-case, right? What can’t be measured, can’t be controlled. So we want to be able to know, and iterate, on our llm workflow until it’s aligned with our risk appetite for whatever workflow.
But that is just the start of the benefits of evals!
What Evals Unlock Beyond Baseline Reliability
- Try different models: You started with GPT 5.2 for your workflow to prove out the concept, fair enough. Now that you have it working though, maybe you can get away with one of the mini models? That can be massive savings, and with evals you can determine exactly where you can, and can’t, make that change.
- Optimize your prompts: Just how much context is needed, anyway? If you’re able to measure you can start playing with alternate prompts and see the varying performance-to-price for your prompts.
- Build trust: Somebody reported a bug? Let’s not rely on ‘seems to be working now’. Instead, you can add the test case to your evals run it, and provide hard numbers for the improvement
- And more! Lure coworkers and end-users into ownership and helping provide eval use-cases. Rapidly prototype workflow adjustments/tweaks. Be appalled at how poorly models a mere six-months-old handle your current workflow.
When you get down to it, evals are not optional. They’re an integral part of any AI-enabled system and the reliability of your workflow cannot be expressed without them. Evals transform vibes from anecdotes to actionable data.
Why 7Rivers
If you’re building (or already running) AI workflows in production, evals are the difference between “it seems fine” and “we can prove it’s safe, accurate, and improving.”
Here at 7Rivers, we make sure your data and workflows, in whatever form they take, have the metrics you need to ensure your workflow remains dependable and flexible for the ever-more-swiftly changing environment.
Want help setting up evals, measuring risk, and hardening your workflow end-to-end? Contact 7Rivers to talk through your use case and get a practical plan for building trustworthy, measurable AI systems.
