What should be in a minimum viable LLM eval suite?

At minimum: 50–100 golden examples covering core use cases, automated scoring for correctness and safety, and a CI gate that blocks deploys on regression.

Can LLMs evaluate other LLMs reliably?

LLM-as-judge works for subjective dimensions (tone, helpfulness) but must be calibrated against human labels. Never use it alone for factual correctness.

How often should you rerun evals?

On every prompt change, model swap, and retrieval index update. Treat evals like unit tests — continuous, not quarterly.

LLM Evaluation Frameworks: How to Measure Quality Before Production Deployment

The gap between an LLM demo and an LLM product is measurement. Stakeholders ask "is it good enough?" Engineering teams answer with anecdotes. Production incidents answer with customer tickets.

Evaluation frameworks close that gap. They turn AI quality from a subjective debate into an engineering discipline — the same way load tests gate releases for traditional software.

Why Evals Are Non-Negotiable

LLM outputs are non-deterministic. Prompt tweaks, model version bumps, and retrieval index changes all shift behavior silently. Without evals:

You cannot detect regressions
You cannot compare models objectively
You cannot justify provider switches to leadership
You cannot satisfy audit requirements for AI in regulated industries

Evals are the contract between AI engineering and the business.

Building a Golden Eval Set

Start with real user intents — support tickets, sales questions, internal queries your system must handle. For each example, define:

Input (user message + context)
Expected behavior (not always exact text — often rubric-based)
Ground truth sources (for RAG: which documents must be cited)
Failure severity (blocker vs. cosmetic)

Aim for 50–100 examples covering happy paths, edge cases, and known adversarial inputs. Expand weekly from production failures.

Automated Scoring Dimensions

Dimension	Method
Factual correctness	Ground-truth match, citation verification
Faithfulness (RAG)	Claim extraction vs. retrieved context
Safety / policy	Rule-based filters + classifier
Format compliance	Schema validation, regex
Latency / cost	Infrastructure metrics

Use LLM-as-judge only for subjective dimensions — and calibrate judges against human labels on a held-out set.

CI Gates for AI

Integrate evals into your deployment pipeline:

Run eval suite on every PR that touches prompts, models, or retrieval
Block merge if blocker-severity scores drop
Store eval history — trend quality over time, not just pass/fail

This is how Sunray Labs AI treats EnTX-class intelligence systems: measurable, versioned, auditable.

Production Monitoring Extends Evals

Pre-deploy evals catch known failures. Production monitoring catches unknown ones:

Sample live traffic for human review
Track user corrections and thumbs-down signals
Alert on drift in embedding space or retrieval scores
Feed failures back into the golden set

Evals are a loop, not a checklist.

Teams that ship LLM features without evals are shipping blind. The cost of building an eval framework is measured in days; the cost of a production incident is measured in trust, revenue, and remediation sprints.

Sunray Labs AI embeds evaluation infrastructure into every deployment — because brilliance in demos means nothing without brilliance in measurement.