AI Evals Engineering — Reliable LLM Quality
Build evaluation suites, golden sets, and CI pipelines that stop silent LLM degradation before users notice.
Duration
6 weeks · 8-10 hours/week
Level
Intermediate
Delivery
Online
Status
Open for enrollment
Why this technology matters.
AI evaluation engineering is the discipline of proving that an AI system actually works — measuring accuracy, groundedness, and behavior change before users discover failures for you. It matters now because LLM outputs are probabilistic and silent regressions are the norm: a prompt tweak or model swap can degrade quality with no error in the logs.
It is used by product and platform teams to gate releases, compare models and prompts, and catch drift in production with golden sets and regression pipelines. It solves the problem of confident iteration on nondeterministic systems, but it does not solve missing product requirements or bad source data — an eval can only measure against a standard you have defined, and it cannot fix retrieval content that does not exist.
By the end you will be able to build an eval dataset with golden expected outputs, a model-and-prompt comparison harness with groundedness and trajectory scoring, and a regression pipeline that blocks degraded releases before they ship.
Why this course exists
The gap is between a demo that looked good once and a production system that stays good across model updates, prompt edits, and shifting user inputs. This course teaches the arc from Model outputs to Prompt variants to Context and Retrieval quality to Tool trajectories to Evaluation gates that make releases safe, so students move from vibes-based testing to repeatable measurement.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and REST APIs
- Familiarity with LLM applications or RAG pipelines
- Basic statistics and evaluation metrics
Technologies & tools
Skills you'll gain
A 6 weeks arc, module by module.
- Module 01
Week 1 — What to evaluate and why
- Module 02
Week 2 — Golden sets and dataset craft
- Module 03
Week 3 — Offline evals: automated judges and metrics
- Module 04
Week 4 — Online evals: sampling, labeling, and feedback loops
- Module 05
Week 5 — Regression testing in CI
- Module 06
Week 6 — Capstone: an end-to-end eval suite
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.
- Author golden sets and eval harnesses for retrieval, generation, and tool-use.
- Run offline, online, and human-in-the-loop evaluations with clear SLOs.
- Wire CI to fail builds on regressions in groundedness and usefulness.
- Present eval results as a product-quality narrative to stakeholders.
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Golden-set eval suite for a Q&A assistant
- Project 02
LLM-as-judge grading pipeline
- Project 03
Regression harness for prompt changes
- Capstone
End-to-end eval system with dashboards and guardrails
Speak the language first.
- Golden test sets
- A curated set of inputs with approved expected outputs used as the stable reference for checking model behavior over time.
- LLM-as-judge scoring
- Using a separate language model with a fixed rubric to grade outputs for qualities like correctness and helpfulness.
- Regression eval pipelines
- Automated checks that rerun the eval set on every prompt or model change to catch silent quality drops.
- Groundedness checks
- Tests that verify a model answer is supported by the retrieved documents rather than invented.
- Task success metrics
- Outcome-level measures such as pass rate or error rate that show whether the system completes real user tasks.
- Human preference ratings
- Structured side-by-side comparisons where reviewers pick the better response to guide quality decisions.
- Failure taxonomy
- A shared set of labels for error types, such as hallucination or refusal, so teams can count and fix them systematically.
- Prompt versioning
- Saving every prompt template with a version number so eval results can be traced back to the exact wording tested.
- Statistical significance for evals
- Checking that a score change is large enough, given the sample size, to trust it is a real improvement.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Golden-set drift makes scores meaningless
Audit failing items to separate real regressions from outdated expectations, then update the goldens with a documented review and re-baseline scores.
LLM judge disagrees with human reviewers
Calibrate the judge on a labeled sample, tighten the rubric with examples, and track judge-human agreement before trusting automated scores.
Small sample sizes hide regressions
Expand the failing slice with targeted cases, stratify results by task type, and require a minimum sample before declaring a win.
Flaky scores from non-deterministic outputs
Fix decoding settings for eval runs, average over multiple runs, and log seeds and model versions with each result.
Eval suite runs too slowly to gate releases
Split into a fast smoke set for every change and a full nightly suite, then cache fixtures and parallelize the slow judges.
Good aggregate score masks a broken slice
Report scores per category and severity, add slice-level thresholds, and block release when any critical slice regresses.
Before you move on, you should be able to
- Explain when to use golden sets, human review, and automated judges
- Design an eval plan with metrics tied to real task outcomes
- Build a regression pipeline that runs on every prompt and model change
- Evaluate groundedness and catch unsupported model claims
- Diagnose eval failures using a shared error taxonomy
- Deploy guardrails so score drops block unsafe releases
- Document eval results and version history for review
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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View CourseReady to start AI Evals Engineering — Reliable LLM Quality?
A 6 weeks course — Online Courses.