AI Evaluator Certification: the full curriculum
24 modules · 30+ hours · 800+ practice questions
The syllabus covers the five skills of human AI evaluation work across 24 modules and 30+ hours: prompt engineering for evaluation tasks (module 1.5), drafting the ideal response (2.0), designing objective rubrics (2.1 to 2.8), grading (1.2 to 1.4) and writing objective justifications (1.6, 3.1 to 3.3), plus platform tools and gating-test practice. No coding; first module free.
Structured training for people who want AI evaluation, AI training or data annotation work: the AI evaluator, AI trainer, AI rater and data annotator roles. Each module below says what it teaches; the course ends with a proctored exam.
- Course
- 24 modules, 30+ hours, 800+ practice questions, self-paced, lifetime access
- Skills
- Five competencies, each mapped to named modules (see the five groups below)
- Tutor
- Kappa, the AI tutor, critiques your reasoning in every lesson and never writes the work for you
- Exam
- 58 questions drawn from across the course, one 120-minute sitting, 75% to pass, camera-proctored with photo ID, closed book; three sittings included.
- Certificate
- issued in the graduate's name on passing, no expiry, verifiable by code at annotation.academy/verify, shareable on LinkedIn under Licenses & Certifications.
- Price
- $249 one-time, installments available; module 1 is free with no card
- Updated
- October 21, 2026
Skill 1
Prompt engineering for evaluation tasks
Modules 1.5
Skill 2
Drafting the ideal response
Modules 2.0
Skill 3
Designing accurate, objective rubrics
Modules 2.1 to 2.8
Skill 4
Grading
Modules 1.2 to 1.4
Skill 5
Writing objective justifications
Modules 1.6, 3.1 to 3.3
What you will be able to do
- 01
Prompt engineering for evaluation tasks
Writing complex, well-specified prompts: instruction hierarchy, constraint types, completeness, multi-turn planning.
- 02
Drafting the ideal response
Describing what a correct, complete answer looks like before judging any candidate.
- 03
Designing accurate, objective rubrics
Atomic criteria, self-containment, objectivity and thresholds, modality-aware and platform-specific rubric patterns.
- 04
Grading
Applying a rubric consistently across cases that do not fit neatly; rating systems, comparison methodology, response-quality dimensions, safety.
- 05
Writing objective justifications
Defending each call in writing in the terms the guidelines use, with citations and source reliability.
Final exam: 58 questions drawn from across the course, one 120-minute sitting, 75% to pass, camera-proctored with photo ID, closed book; three sittings included. Each module ends with a practice quiz (75% to pass, unlimited retries, full answer review).
Certificate: issued in the graduate's name on passing, no expiry, verifiable by code at annotation.academy/verify, shareable on LinkedIn under Licenses & Certifications.
Core Competencies of AI Evaluators
Master the 4 core competencies of AI evaluators and learn cognitive load management strategies for consistent evaluation work.
- Understand the 4 core competencies that define successful AI evaluators
- Learn to manage cognitive load for sustained evaluation quality
- Develop systematic approaches to evaluation work
Topics: four core competencies, five skills of AI evaluation work, instruction following, error detection, clear feedback
Modality-Specific Assessment
Evaluate AI outputs across text, code, image, and multi-modal modalities. Recognize modality-specific quality cues and failure patterns.
- Identify different content modalities in AI evaluation tasks
- Understand why modality-specific criteria are essential
- Classify tasks by primary and secondary modalities
Topics: modality, text evaluation, audio evaluation, image evaluation, video evaluation
How AI Training Works
Learn about RLHF, the three stages of AI training, different evaluation task types, and the quality standards evaluators are held to.
- Understand what RLHF is and why it matters
- Identify the three stages of RLHF
- Recognize different types of evaluation tasks
Topics: RLHF, reinforcement learning from human feedback, supervised fine-tuning, reward model training, evaluation task types
Core Evaluation Skills
Master comparison methodology and reading & interpreting rubrics effectively, applying guidelines consistently.
- Apply the five-step systematic comparison framework instead of trusting a first-read impression
- Set the two or three decision criteria that matter for a prompt before reading any response
- Rank three or more responses with a consistent K-way strategy and resolve close calls
Topics: comparison methodology, rubric reading, rubric interpretation, systematic comparison, decision making
Response Quality Assessment
Learn to evaluate AI responses across key dimensions: accuracy, hallucination detection, instruction following, and failure type hierarchy.
- Score helpfulness against the user's intent rather than the literal question, and spot pleasantry and repetition anti-patterns
- Classify accuracy errors (factual, logical, outdated, hallucination, precision) and trace each to a recognition, reasoning or calculation origin
- Evaluate accuracy on a topic outside your own expertise
Topics: five core dimensions, helpfulness, accuracy, safety, instruction following
Safety Fundamentals
Understand AI safety principles, harm categories, and how to evaluate responses for safety compliance.
- Recognize the six harm categories: physical, psychological, financial, reputational, societal and legal
- Weigh severity against likelihood and give vulnerable populations extra weight
- Spot the five red flags for unsafe content and the common disguises: educational, hypothetical, role-play and incremental framing
Topics: AI safety, harm categories, harmful content, safety evaluation, risk assessment
Prompt Engineering for Evaluation Tasks
Master prompt decomposition, quality criteria identification, and structured evaluation approaches for different prompt types.
- Understand why prompt quality matters for AI training
- Recognize prompt layers and the instruction hierarchy
- Apply the prompt completeness checklist to assess prompts
Topics: prompt quality, instruction hierarchy, prompt layers, core model instructions, model customization instructions
Justification Writing
Write clear, defensible justifications with the four-part structure: conclusion, criteria, evidence, connection. Master evidence-based reasoning for evaluation decisions.
- Write evidence-based evaluation rationales
- Structure effective justifications using the dimension reference framework
- Apply the Pyramid Principle to lead justifications with the verdict
Topics: justification structure, evidence-based reasoning, pyramid principle, decisive dimensions, deviation
Data Annotation Fundamentals
Master annotation taxonomies, labeling guidelines, and quality control for structured AI training data.
- Understand the connection between data annotation and AI evaluation
- Master image annotation types
- Apply text annotation techniques
Topics: image/text/audio/video annotation, IoU, inter-annotator agreement, flag vs skip, annotation quality metrics
Ideal Response Description & Rubric Properties
Create Ideal Response Descriptions through prompt analysis. Master criterion properties: atomicity, self-containment, objectivity, specificity, weighting, and golden responses.
- Understand what rubrics are and how their structure varies across projects
- Identify the components a rubric may include and their purpose
- Create Ideal Response Descriptions (IRDs) through systematic prompt analysis
Topics: rubric definition, analytic rubrics, holistic rubrics, rubric components, rationale
Atomicity Bootcamp
Recognize and decompose non-atomic criteria. Write atomic criteria from scratch and apply atomicity reliably across any task type.
- Define atomicity in the context of evaluation criteria
- Recognize non-atomic criteria instantly (within 30 seconds)
- Decompose multi-aspect criteria into atomic components
Topics: atomicity, atomic criteria, non-atomic criteria, criterion decomposition, the AND test
Instance-Specific Mastery
Write criteria tied to the specific instance, not to generic task templates. Surface what this prompt requires that others would not.
- Distinguish between generic and instance-specific criteria
- Write criteria that reference actual task content
- Avoid a leading cause of evaluation quality failures
Topics: instance-specificity, instance-specific criteria, prompt-specific requirements, generic vs specific criteria, template criteria
Self-Containment
Write criteria a reviewer can apply without consulting external materials. Eliminate hidden dependencies and implicit context.
- Define self-containment in evaluation criteria
- Recognize cross-referencing violations
- Write criteria that stand alone
Topics: self-containment, standalone criteria
Objectivity & Thresholds
Convert subjective judgments into concrete thresholds. Build criteria two reviewers would score the same way.
- Understand the objectivity-subjectivity spectrum and why rubric projects lean objective
- Distinguish between objective and subjective criteria
- Write measurable criteria with concrete thresholds
Topics: objectivity, thresholds, subjective vs objective criteria, measurable thresholds, scoring consistency
Modality-Aware Rubrics
Apply rubric properties to real tasks. Master modality-aware evaluation, platform patterns, speed drills, and integration practice.
- Integrate all five core properties into complete rubrics
- Build modality-specific criteria for audio and video tasks
- Apply the Complete Rubric Checklist before every submission
Topics: modality-aware criteria, the five core properties, audio criteria, video criteria, multimodal integration
Platform Rubric Patterns
Recognize the rubric patterns each major platform expects. Adapt your rubric writing to platform conventions without losing rigor.
- Recognize common rubric patterns across platforms
- Adapt evaluation approach to different platform styles
- Build platform-agnostic transferable skills
Topics: platform rubric patterns, rubric adaptation
Rubric Speed Drills
Build the muscle to draft sound rubrics under platform-realistic time limits. Cut deliberation time without dropping rubric quality.
- Build rubrics quickly while maintaining quality
- Develop muscle memory for criteria writing
- Know your pace per criterion so any rubric can be budgeted by its size, and follow platform time guidance
Topics: rubric speed, speed drills, rubric efficiency
Integration Practice
Combine rubric writing, evaluation, and justification on end-to-end tasks that mirror real platform workflows.
- Apply all five properties simultaneously
- Build complete certification-ready rubrics
- Handle complex real-world scenarios
Topics: five core properties, complete rubric checklist, multimodal rubrics, handling ambiguity, integration practice
Citation Speed Training
Find and format citations rapidly. Master source reliability evaluation and platform citation formats.
- Find citations rapidly (5 claims in 10 minutes)
- Verify factual accuracy efficiently
- Use reliable sources quickly
Topics: citation, fact-checking, source reliability, verification workflow, source evaluation
Source Reliability
Distinguish strong, weak, and unreliable sources. Apply a repeatable check to every citation before you accept it as evidence.
- Evaluate source credibility quickly
- Identify bias and conflicts of interest
- Distinguish primary from secondary sources
Topics: primary vs secondary sources, bias and conflicts of interest, red flags, Triangle Method
Platform Citation Formats
Format citations to match each platform's expected style. Move fast without tripping on platform-specific format rules.
- Format citations correctly for different platforms
- Meet platform-specific citation requirements
- Build consistent citation habits
Topics: citation formats, platform citation styles, citation formatting
Platform Navigation & Tools
Navigate platform interfaces and tooling efficiently. Reduce friction so your evaluation time goes into the work, not the UI.
- Navigate platform interfaces efficiently
- Master common platform tools and features
- Understand task queues and workflows
Topics: platform navigation, platform interfaces, common platform tools, task queues, workflow optimization
Time Management & Productivity
Run an evaluation session with a sustainable cadence. Protect attention across the hours where careless mistakes pile up.
- Budget time effectively for different task types
- Make the most of your working time through efficiency
- Maintain consistent quality under time pressure
Topics: time management, productivity, fatigue
Gating Test Simulations
Walk into the gating exam knowing what to expect. Practice on representative tasks under representative time pressure.
- Understand gating test formats and requirements
- Practice certification-level evaluation scenarios
- Master test-taking strategies for qualification exams
Topics: gating tests, gating exam, exam preparation, time pressure, qualification tests
What Annotation Academy is, and is not
Three different fields use the words AI evaluation. This program is for one of them: the people who grade AI, not the people who build it.
It is
- Training and certification for human AI evaluation, AI training and data annotation work: the AI evaluator, AI trainer, AI rater and data annotator roles on platforms such as Outlier, DataAnnotation, Alignerr, Ethos, Mercor and Micro1.
- Five skills: prompt engineering for evaluation tasks, drafting the ideal response, designing objective rubrics, grading, and writing objective justifications.
- Independent: not affiliated with any platform or AI lab. The certificate is earned through an ID-verified, proctored exam and is verifiable by its code.
- Open to beginners: no coding or AI background needed. The first module is free.
It is not
- An ML, data-science or coding course. We do not teach model building, fine-tuning or evaluation frameworks.
- An LLM app-development course or a prompt-engineering course for developers.
- A general AI-literacy course.
- A job-placement service. Platforms do not require the certification; it trains the skills the work requires, which is also what platform assessments test.
This is the non-technical, human-rater track for AI evaluation, AI training and data annotation work. It is not an LLM-as-a-judge, MLOps or Python course; for those, the engineering-side options are DeepLearning.AI's Evaluating AI Agents (free short course) and eval.qa (free, 147 lessons).
Questions about the training
Straight answers about what this course is, who it is for and how it runs.
What course should I take to become an AI evaluator or AI trainer?
One built for the human side of the work, which platforms post as AI evaluation, AI training or data annotation. The AI Evaluator Certification teaches the five skills the work requires (which is also what platform assessments test): prompt engineering for evaluation tasks, drafting the ideal response, designing objective rubrics, grading, and writing objective justifications, with Kappa, an AI tutor, critiquing your reasoning in every lesson. Start with the free first module and judge the teaching for yourself.
Is this an ML, data-science or coding course?
No. It does not teach model building, fine-tuning, evaluation frameworks or Python. It teaches the judgment work a human evaluator does on a platform: reading guidelines, grading responses, comparing outputs and defending each call in writing. No coding or AI background is needed to start.
Do platforms require this certification?
No platform requires it. Each platform runs its own onboarding and assessments; the certification trains the skills the work requires (which is also what those assessments test) and gives you an ID-verified, proctored credential that anyone can verify by its code. It is a skills credential, not a job offer.
How long does the training take?
The 24 modules are about 30 hours of lessons and practice. There is no deadline and access does not expire, so most people spread them over a few weeks around other work, then sit the proctored exam when they are ready.
Is the first module free?
Yes. Module 1 is free with no card required, and it is the real first module, not a preview. If the teaching suits you, the full certification is a one-time payment with installments available; see the pricing page.
Ready to start?
Your first module is free. No credit card required.