EvalBV-09

Lab

Blueprint Vault

Starter Pilot Pack

AI Evaluation Workflows

Model-risk interfaces and eval gates before probabilistic steps hit regulated workflows.

Starter Pilot Pack · BV-09

AI Evaluation Workflows

Model-risk interfaces and eval gates before probabilistic steps hit regulated workflows.

Risk, compliance, and ML platform teams

Who it's for

Risk, compliance, and ML platform teams

Best when

Eval harnesses · MRM checklists · golden sets

Outcome

Monday: run the eval harness on sample gold cases and review MRM checklist templates with your risk team.

Pilot templates and reference implementations only—not production-authorized guardrails, certified controls, or legal/compliance sign-off.

Preview only — not yet available for purchase

INSTRUCTIONS.md

Start here — local pilot steps (Architecture Mapping Session link in purchase receipt email)

Engineer README

Local dry-run setup, env vars, and pilot verification checklist

Prompt & schema blueprint

Reference prompt templates and structured output contracts (not prod-tuned)

GCP + Vertex architecture

Reference diagram: reasoning tier, validator demo app, audit sink

Reference demo apps

Illustrative Python validators, JSON schemas, and eval harness examples

Reference Terraform

Optional IaC sketch for GCP — not prod-hardened

ROI cost calculator

Model-mix and volume inputs → monthly inference spend and deflection ROI

Disclaimer & license

Pilot-only scope — not production-authorized guardrails

Blueprint Lab

Segment eval gates — promote, hold, and fail paths with gold-dataset trace. Free when signed in.

Your pilot week

Your path this week

Monday: run the eval harness on sample gold cases and review MRM checklist templates with your risk team.

Golden datasets drive segment gates; Python returns PROMOTE | HOLD | FAIL with rule_id before model promotion.

Local eval harness dry-run on gold dataset
Review segment gate thresholds and report templates
Align promote/hold/fail paths with your MRM binder
After purchase, use the Architecture Mapping Session link in your receipt email to schedule your review

Segment eval gates — promote, hold, and fail paths with gold-dataset trace. Free when signed in.

Inside this edition

01Executive snapshot
02Hero architecture
03Week-one checklist
04GCP reference

Executive snapshot

Operators shipping ai evaluation workflows need deterministic controls around probabilistic steps—validators, budgets, and audit logs before tools execute in production.

Pilot outcome: run the local dry-run demo, review prompt templates, and bring stack questions to your Architecture Mapping Session.

Constraint	Blueprint answer
Abuse / cost	Fail-closed Python gates with rule_id trace
Audit	Append-only decision log per request_id
Week one	Local eval harness + architecture diagram review

Hero architecture

Request → reasoning tier (structured plan)
       → validate_pre()  [FAIL CLOSED]
       → worker / tools
       → validate_post() [FAIL CLOSED]
       → human gate (tiered)
       → log
```…

Remaining chapters, Starter Pilot Pack download, and Blueprint Assistant unlock with purchase.

AI Evaluation Workflows

Model-risk interfaces and eval gates before probabilistic steps hit regulated workflows.

Risk, compliance, and ML platform teams

Who it's for

Risk, compliance, and ML platform teams

Best when

Eval harnesses · MRM checklists · golden sets

Outcome

Monday: run the eval harness on sample gold cases and review MRM checklist templates with your risk team.

Pilot templates and reference implementations only—not production-authorized guardrails, certified controls, or legal/compliance sign-off.

Preview only — not yet available for purchase

Your path this week

Monday: run the eval harness on sample gold cases and review MRM checklist templates with your risk team.

Golden datasets drive segment gates; Python returns PROMOTE | HOLD | FAIL with rule_id before model promotion.

Local eval harness dry-run on gold dataset

Review segment gate thresholds and report templates

Align promote/hold/fail paths with your MRM binder

After purchase, use the Architecture Mapping Session link in your receipt email to schedule your review

Executive snapshot

Operators shipping ai evaluation workflows need deterministic controls around probabilistic steps—validators, budgets, and audit logs before tools execute in production.

Pilot outcome: run the local dry-run demo, review prompt templates, and bring stack questions to your Architecture Mapping Session.

Constraint	Blueprint answer
Abuse / cost	Fail-closed Python gates with rule_id trace
Audit	Append-only decision log per request_id
Week one	Local eval harness + architecture diagram review