Enterprise LLM Evals · Lesson 12

Capstone: Enterprise Eval Program Design

A reusable blueprint for any production AI application

Capstone: Design an Enterprise Eval Program

Lesson 12 — a reusable blueprint for any production AI application

What you'll learn
  1. How to design a full enterprise eval program from scratch
  2. A fill-in template for any AI application
  3. Five capstone scenarios to practice on
  4. What mastery looks like and how to prove it

Core idea

A production eval program ties product quality, security, operations, and governance into one release system.

1. Capstone Template

Use case: Users: Data sources: AI architecture: single-turn / workflow / RAG / agent / multi-agent High-risk actions: Failure modes: Golden dataset splits: Metrics: Thresholds: Human review plan: Red-team plan: Online monitoring: Release gates: Owner + cadence:

2. Example Release Decision

AreaGateStatus
Correctness>= 85% on golden test splitRequired
RAG groundedness>= 90%Required
PII leakage0 critical failuresRequired
Tool authorization0 unauthorized actionsRequired
Latencyp95 under agreed SLORequired
Human reviewSignoff on high-risk sampleRequired
MonitoringTracing + feedback + alerting liveRequired

3. Five Capstone Scenarios

Practice designing eval programs for
  1. HR Policy Assistant — RAG over internal docs, role-based access, PII handling, escalation to HR
  2. Customer Support Refund Agent — intent classification, tool selection, refund policy compliance, unauthorized refund prevention
  3. SQL Analytics Copilot — SQL validity, execution correctness, no destructive queries, PII masking, query cost limits
  4. Legal Contract Summarizer — clause extraction recall, factual consistency, omission risk, unsafe legal advice refusal
  5. Multi-Agent Research Workflow — handoff accuracy, source quality, citation correctness, contradiction detection, prompt injection from web pages

4. Final Mental Model

Evals are the enterprise replacement for vibes. Without evals: "It looked good in demo." With evals: "Version 17 passed the release suite, regressed 2% on edge cases, has 0 critical safety failures, and is monitored in production."

5. What Mastery Looks Like

You are a master when you can look at any enterprise AI system and answer:

  1. What can fail?
  2. How would we know before release?
  3. How would we know after release?
  4. Which failures block launch?
  5. Which failures need human review?
  6. How do production failures improve the eval suite?
  7. What evidence would satisfy engineering, product, security, compliance, and leadership?

Proof-of-Mastery Portfolio

Build these artifacts
  • One RAG eval suite — golden dataset, retrieval metrics, groundedness/citation checks, access-control tests
  • One agent eval suite — trace-level scoring, tool selection/argument checks, task completion and safety gates
  • One red-team suite — prompt injection, jailbreaks, PII/secret leakage, data exfiltration, unauthorized tool use
  • One production monitoring design — traces, online scores, user feedback, latency/cost dashboards, alert thresholds
  • One governance packet — eval plan, risk map, release gates, run results, sign-off and residual risk notes
⚡ Final Drill
Q1: Design evals for an enterprise HR policy bot.
Show approach

Include RAG groundedness/citation correctness, HR policy correctness, role-based document access (User A cannot see User B's salary docs), PII handling (no SSN/DOB in responses), unsafe legal/medical advice refusal, escalation behavior (route complex cases to HR), latency/cost, human review by HR/legal team, and production feedback monitoring with failure-to-regression loop.

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