LLM Evaluation & Guardrails Package. @llm-eval-guardrails

The LLM Evaluation & Guardrails Package puts engineering discipline around an LLM-powered feature you already have or are about to ship. Teams routinely change a prompt, eyeball three outputs, and deploy — until the day a regression, a jailbreak, or a hallucinated claim reaches customers. The finish state of this sprint: your feature has versioned test sets with automated scoring, documented red-team results, runtime guardrails on inputs and outputs, and a release gate in CI that blocks any change that degrades quality or safety below the agreed bar.

Scrums.com treats hallucination rate, latency, and token cost as first-class delivery metrics on every GenAI build; this package installs that measurement culture on your feature, whoever built it.

Evaluation Test Sets & Scoring

Test sets built from real usage and edge cases, with automated scoring fit to the task — exact checks where possible, calibrated LLM-as-judge with human agreement checks where not — all versioned alongside the code.

Red-Team & Adversarial Checks

Structured adversarial testing: prompt injection, jailbreaks, data exfiltration attempts, off-policy content, and domain-specific failure modes — findings documented with reproductions and fixes prioritized.

Runtime Guardrails

Input and output controls in production: injection filtering, PII handling, topic and policy enforcement, grounding checks where claims must be sourced, and safe fallback behavior when a check fails.

CI Release Gates & Dashboards

Evaluations wired into CI so prompt, model, and retrieval changes run the suite before deploy, with score thresholds as merge gates and dashboards tracking quality, safety, latency, and cost over time.

1. Scope — Define the quality and safety bars, the failure modes that matter, and the metrics per capability.
2. Build — Build test sets, scoring, guardrails, and red-team coverage; wire the release gate into your CI.
3. Handover — Gates live and blocking, dashboards running, and a playbook for growing the suite as the feature evolves.

The LLM Evaluation & Guardrails Package is a menu item on the Scrums.com delivery catalog, available at every plan tier. Add it to your plan backlog and your delivery team schedules it like any other item — scoped, tracked, and reported through the SEOP. See Delivery Plan Tiers.

Our AI feature was built by another vendor. Does that matter?

No. The package evaluates behavior at the boundaries — inputs, outputs, and traces — so it wraps any implementation. It is a common first step before deciding what to fix.

Are LLM-as-judge scores trustworthy?

Only when calibrated, which is why judge prompts are validated against human-labeled samples and agreement is reported. Where deterministic checks are possible, they are always preferred.

Who maintains the test sets after handover?

Your team, using the playbook — evaluation only works as a living practice. Teams that want it run for them long-term pair this with the Model Ops Retainer from the menu.