Enterprise LLM Evals Track

Enterprise LLM Evals

A complete learning track on evaluation for production AI systems

Enterprise LLM Evals: A Complete Learning Track

12 lessons. From "what are evals?" to designing production-grade evaluation systems for any AI application.

Why this track exists

Most LLM evaluation content stops at "use BLEU score" or "try Ragas." Enterprise teams need more: golden datasets, LLM-as-judge calibration, RAG metrics, agent trace evaluation, red-team suites, CI/CD gates, production monitoring, and governance evidence.

This track is what I wish I had when I started building production AI apps. Every lesson is structured, visual, and tied to real enterprise decisions — not vendor demos.

What you'll be able to do

Explain the 4 eval families: model, application, risk, and ops
Build golden datasets with normal, edge, adversarial, and historical cases
Design rubrics and calibrate LLM-as-judge against human labels
Evaluate RAG systems: retrieval quality, groundedness, citations, access control
Evaluate agents: tool selection, arguments, task completion, handoffs
Run red-team suites for prompt injection, jailbreaks, PII leakage, and tool misuse
Set up CI/CD eval gates and production monitoring
Choose the right tools: LangSmith, MLflow, Ragas, DeepEval, Promptfoo, Giskard
Produce governance evidence for compliance and audit readiness
Design a full enterprise eval program from scratch for any AI application

The core idea

Enterprise LLM evals are not a single metric. They are a control system: pre-deployment gates, post-deployment monitoring, and governance evidence — all versioned, all auditable.

Curriculum

Module 1 — Foundations

The Enterprise Eval Map

Model evals vs app evals vs risk evals vs ops evals. Why MMLU is not enough.

Golden Datasets, Rubrics & LLM-as-Judge

How enterprise teams define "good" before automating scoring. Dataset anatomy, rubric design, judge calibration.

Module 2 — Architecture-Specific Evals

RAG Evaluation

Retrieval quality, context precision/recall, faithfulness, citation correctness, access control.

Agent & Tool Evaluation

Trace-level evals: planning, tool selection, arguments, handoffs, task completion, authorization.

Multimodal & Specialized Evals

Code, SQL, summarization, classification, vision, voice. Execution-based vs judge-based evals.

Module 3 — Risk & Production

Safety, Security & Red-Team Evals

Prompt injection, jailbreaks, PII leaks, OWASP/NIST framing, adversarial test suites.

Offline Evals, CI Gates & Online Monitoring

Release gates, regression thresholds, production traces, drift detection, feedback loops.

Cost, Latency & Reliability Evals

Budget gates, SLOs, fallback models, token economics, reliability patterns.

Module 4 — Enterprise Operation

Enterprise Tooling Landscape

LangSmith, MLflow, Ragas, DeepEval, Promptfoo, Giskard, Phoenix, Langfuse, Datadog, cloud suites.

Governance, Risk & Compliance

NIST AI RMF, OWASP LLMSVS, audit evidence, release sign-offs, residual risk documentation.

Human Feedback & Judge Calibration

Annotation queues, pairwise review, judge-human agreement, disagreement analysis.

Capstone: Design an Enterprise Eval Program

A reusable blueprint for any production AI application. Includes 5 capstone scenarios.

How to use this track

Read one lesson, then answer its practice drill out loud. Don't just read passively. Enterprise eval fluency comes from being able to design evals for a new architecture quickly — not from memorizing tool names.

Prerequisites

You should know what an LLM is, have called an LLM API before, and understand basic prompt engineering. That's it. No ML degree, no eval framework experience required.

Sources & Further Reading

Want to see these patterns in action?

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