HIGAET AI Evals Engineering
Master offline and online evaluation for AI systems, covering datasets, judges, guardrails, regression suites, and production quality monitoring.
Duration
6 weeks · 8-10 hours/week
Level
Advanced
Delivery
Online
Status
Open for enrollment
Why this technology matters.
AI evals engineering is how teams measure whether an AI system actually works, using offline test suites, judges, and live production monitors. It matters now because fluent output can hide regressions, unsafe refusals, and slow quality drift.
Engineering, product, and operations teams use evals to test prompts, retrieval, and agents before release and to watch quality after launch. It solves repeatable scoring with gold sets and rubrics, regression detection, and drift and toxicity monitoring, but it does not fix a bad product idea, missing data, or unclear success criteria — measurement alone does not improve the system.
By the end you will be able to build an offline eval suite with gold sets and task rubrics, a scoring setup with LLM-judge and programmatic checks, and a regression suite plus an online monitor for drift, toxicity, and refusal behavior.
Why this course exists
The gap is between eyeballing a few outputs and running a production quality system with gold sets, judges, regression gates, and live monitors. This course teaches the arc from Prompt, Context, Retrieval, Tools, and Agents through Evaluation to Security and Production, so students can prove quality and catch regressions.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Familiarity with LLM APIs and prompts
- Comfortable with Python and datasets
- Basic statistics or quality-measurement concepts
Technologies & tools
Skills you'll gain
A 6 weeks arc, module by module.
- Module 01
Module 01 — Foundations: Eval types, metrics, and quality dimensions
- Module 02
Module 02 — Core: Dataset curation, gold sets, and rubric design
- Module 03
Module 03 — Core: LLM judges, scoring functions, and calibration
- Module 04
Module 04 — Engineering: Regression suites for RAG and agents
- Module 05
Module 05 — Production: Guardrails, online monitoring, and alerting
- Module 06
Module 06 — Capstone: Deliver an eval harness with dashboards and reports
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Build offline eval suites with gold sets and task rubrics
- Design LLM-judge and programmatic scoring methods
- Develop regression tests for prompts, retrieval, and agents
- Deploy online monitors for drift, toxicity, and refusal behavior
- Integrate guardrails and policy checks into serving paths
- Evaluate agreement rates, calibration, and failure clusters
- Secure eval data with sampling, masking, and access controls
- Optimize eval runtime, sampling strategy, and alert thresholds
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Offline eval suite with gold sets
- Project 02
LLM-judge scoring pipeline
- Project 03
Prompt and retrieval regression suite
- Capstone
Production quality monitoring system with guardrails
Speak the language first.
- Offline eval suites
- Repeatable test sets run before release to measure AI quality without live users.
- Gold sets
- Curated examples with approved answers used as the reference standard for scoring.
- Task rubrics
- Written scoring rules that define what counts as a correct or high-quality response.
- LLM judges
- Models configured to score outputs against a rubric when human review is too slow.
- Programmatic scoring
- Rule-based checks, such as exact match or citation presence, that score outputs automatically.
- Regression tests
- Saved prompt and retrieval cases rerun after changes to catch behavior that got worse.
- Online monitors
- Live dashboards that track quality signals such as drift, toxicity, and refusal rates.
- Quality drift
- Gradual change in model behavior over time that moves results away from the approved standard.
- Guardrails
- Filters and policies that block unsafe or off-topic outputs before they reach users.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Gold set scores drift from human judgment
Sample disagreements, refresh stale references and rubric wording, and re-calibrate the gold answers.
LLM judge disagrees with reviewers
Tighten the rubric with examples, lower judge temperature for consistency, and measure agreement on a labeled subset.
Regression suite misses real failures
Add failing production cases to the suite, tag them by prompt, retrieval, or agent cause, and rerun on every change.
Online monitor fires false alarms
Adjust alert thresholds on clean baseline data and separate real drift from normal traffic variation.
Toxicity or refusal spikes in production
Correlate the spike with recent prompt or data changes, tighten guardrail rules, and roll back the offending change.
Before you move on, you should be able to
- Build offline eval suites with gold sets and task rubrics
- Design LLM-judge and programmatic scoring methods
- Develop regression tests for prompts, retrieval, and agents
- Deploy online monitors for drift, toxicity, and refusal behavior
- Evaluate eval reliability against human judgment
- Explain how offline and online evaluation complement each other
- Build quality gates that block releases on eval failures
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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A 6 weeks course — AI & Generative Intelligence.