AI Trainer

Program

AI Trainer

Shape how AI models learn and behave

Learn prompt engineering, data labeling, and how model feedback loops work.

8

Weeks

40

Chapters

4

Practice formats

1

Portfolio artifact

8 weeks — one module per week (8 modules, 40 chapters), each week ending with a live class every Saturday, plus a module exam before the next module opens.

Curriculum

40 chapters across 8 modules — each with guided notes, a live example, a quiz, an exercise, a challenge, and class practice.

Module 1 · FOUNDATIONS OF AI TRAINING

  1. 1Introduction to AI TrainingWhat AI trainers do, why human judgment matters in AI, the kinds of tasks involved, and the skills the role needs.Learn →
  2. 2How Large Language Models WorkTokens, next-token prediction, pretraining and fine-tuning, context windows, sampling and temperature, and why models hallucinate.Learn →
  3. 3How Human Feedback Shapes ModelsSupervised fine-tuning, preference data, reward models, RLHF and related methods, evaluation, and how trainer judgments become model behavior.Learn →
  4. 4Tokens, Context Windows & SamplingHow text becomes tokens, why context windows matter, how temperature and sampling shape outputs, and what this means when you evaluate responses.Learn →
  5. 5AI Model Types, Capabilities & LimitsText, vision, speech and multimodal models; what they're good and bad at; tool use and retrieval; and matching evaluation approaches to model types.Learn →

Module 2 · DATA LABELING & QUALITY

  1. 6Data Labeling FundamentalsLabeling task types for text, images, and audio, label sets, majority vote, class balance, and working accurately at scale.Learn →
  2. 7Writing Annotation GuidelinesWhat good guidelines contain, definitions and examples, decision trees, edge cases, and keeping guidelines up to date.Learn →
  3. 8Quality, Agreement & CalibrationPercent agreement and Cohen's kappa, gold-standard questions, calibration sessions, adjudication, and quality metrics.Learn →
  4. 9Image Annotation & Bounding BoxesImage annotation types (classification, boxes, polygons, keypoints), box-drawing rules, occlusion and truncation, and measuring box quality with IoU.Learn →
  5. 10Text Span Annotation & Named EntitiesSpan labelling for named entities, boundaries and nesting, entity type definitions, inter-annotator agreement on spans, and precision/recall against gold.Learn →

Module 3 · EVALUATING MODEL OUTPUTS

  1. 11Writing Evaluation RubricsEvaluation dimensions, rating scales with anchors, weighting and severity, justifications, and testing a rubric for consistency.Learn →
  2. 12Evaluating Accuracy & HelpfulnessChecking instruction following, verifying facts, checking reasoning, math, and code, judging helpfulness, and writing strong justifications.Learn →
  3. 13Preference Ranking & ComparisonsPairwise comparisons, preference strength, ties, ranking multiple responses, common rater biases, and turning preferences into scores.Learn →
  4. 14Safety, Bias & Harm ReviewCategories of harm, appropriate versus unnecessary refusals, bias and fairness, privacy, and reviewing sensitive content responsibly.Learn →
  5. 15Evaluating Instruction Following & FormatBreaking prompts into explicit constraints, checking length, format, required content and tone, scoring compliance, and separating instruction-following from quality.Learn →

Module 4 · PROMPTS & PROFESSIONAL PRACTICE

  1. 16Prompt EngineeringWriting clear prompts, roles and context, examples (few-shot), output formats, step-by-step reasoning, and iterating on prompts.Learn →
  2. 17Writing Ideal Responses (Demonstrations)What makes an ideal response, following style guides, structure and formatting, accuracy and sourcing, and editing model drafts.Learn →
  3. 18Testing & Iterating PromptsBuilding test sets for prompts, pass/fail criteria, comparing prompt versions, guarding against overfitting, and documenting prompt changes.Learn →
  4. 19Style Guides for AI ResponsesWhy projects define response style, common style rules (tone, structure, formatting, hedging), applying a style guide consistently, and giving style feedback.Learn →
  5. 20Productivity, Quality & EarningsBalancing speed and quality, effective hourly rates per task type, quality thresholds, planning a working week, and avoiding burnout.Learn →

Module 5 · ON THE JOB: SPECIALIST DOMAIN TASKS

  1. 21On the Job: Evaluating Code ResponsesCoding tasks are some of the best-paid AI training work. Judge model-written code for correctness, edge cases, efficiency, security and explanation quality — by running it, not just reading it.Learn →
  2. 22On the Job: Evaluating Maths & Step-by-Step ReasoningSTEM tasks ask you to check multi-step reasoning, not just final answers. Verify each step, spot where reasoning breaks, write process-level feedback, and craft rigorous ideal solutions.Learn →
  3. 23On the Job: Multilingual & Localisation EvaluationEvaluate model responses in your languages — fluency, accuracy, cultural appropriateness, locale conventions — using error taxonomies, and write natural ideal responses for native speakers.Learn →
  4. 24On the Job: Evaluating Data Analysis ResponsesChecking a model's numbers, recomputing statistics, spotting misleading conclusions, verifying code and charts, and writing clear correction notes.Learn →
  5. 25On the Job: Evaluating Creative WritingJudging stories, poems and marketing copy: following the brief, originality, voice, structure, avoiding clichés, and giving fair, specific feedback on subjective work.Learn →

Module 6 · ON THE JOB: ADVANCED EVALUATION & RED TEAMING

  1. 26On the Job: Red Teaming for SafetyRed-teaming projects pay trainers to find where models behave unsafely. Learn the threat categories, systematic test design, safe documentation, severity rating, and the strict ethics of the work.Learn →
  2. 27On the Job: Evaluating Agents & Tool UseNewer projects grade AI agents that browse, call APIs and edit files. Evaluate multi-step trajectories — goal completion, tool choice, arguments, error recovery, safety and efficiency.Learn →
  3. 28On the Job: Fact-Checking & Grounding EvaluationJudge whether responses are true and supported — break answers into claims, verify against sources, rate citations, catch subtle hallucinations, and write calibrated ideal responses.Learn →
  4. 29On the Job: Evaluating Refusals & Over-RefusalThe two failure modes of safety behaviour — unsafe compliance and over-refusal — judging refusal quality, borderline requests, and tracking both rates across model versions.Learn →
  5. 30On the Job: Evaluating Summaries & Long-Context AnswersFaithfulness, coverage and concision in summaries, checking claims against long source documents, position bias, and efficient verification strategies.Learn →

Module 7 · ON THE JOB: WORKING ON ANNOTATION PROJECTS

  1. 31On the Job: Quality, Speed & Platform WorkflowsHow AI training work actually runs — onboarding assessments, task queues, reviewer audits, quality scores, throughput targets, guideline updates, and how to stay on high-paying projects.Learn →
  2. 32On the Job: Reviewer & Team Lead — Running Quality AssurancePromoted to reviewer: audit others' work fairly, run calibration sessions, measure agreement, give actionable feedback, propose guideline updates, and report quality to the client.Learn →
  3. 33On the Job: Privacy, Ethics & Wellbeing in AI Training WorkHandle personal data and confidential client material correctly, recognise ethical issues in tasks, protect yourself when exposed to disturbing content, and know your rights as a contractor.Learn →
  4. 34On the Job: Guideline Updates & Edge-Case EscalationHow guidelines evolve, reading change logs, applying updates consistently, escalating ambiguous cases, and contributing examples that improve guidelines.Learn →
  5. 35On the Job: Running AI Training Work as a BusinessFreelance AI training work: estimating monthly income, unpaid admin time, setting aside tax, tracking costs, diversifying platforms, and planning hours for income goals.Learn →

Module 8 · CAPSTONE & CAREER LAUNCH

  1. 36Capstone: Rubric & Graded OutputsBuild an evaluation rubric, test it for agreement, grade a set of model outputs with justifications, and present your judgment process.Learn →
  2. 37Applying for AI Training Work & Passing AssessmentsGet accepted onto good projects — position your domain and language expertise, prepare for qualification tests, build a small evidence portfolio, and grow toward reviewer and specialist roles.Learn →
  3. 38Building an AI Trainer PortfolioShowcasing evaluation skills without breaking confidentiality: sample rubrics, graded examples, guideline writing, domain expertise, and presenting your work.Learn →
  4. 39Career Paths in AI Evaluation & DataWhere AI training experience leads: reviewer and QA lead, guideline writer, evaluation specialist, red teamer, data operations, and skills to develop for each.Learn →
  5. 40Your First 90 Days as an AI TrainerBuild a strong start — master your first project's guidelines, establish quality before speed, use feedback systematically, manage your schedule and income, and position for promotion.Learn →
Start learning →

Delivery

Blended — self-paced lessons plus live calibration sessions

Portfolio outcome

A labeling/evaluation rubric and a set of graded model outputs, demonstrating your judgment process.

Certificate

Certificate of completion, verifiable online with its credential code.

By the end of this program, you'll be job-ready to…

  • Write rubrics precise enough for two people to score the same way
  • Evaluate model outputs for accuracy, safety, and bias
  • Explain how human feedback becomes model behavior

Salary expectations

Estimated U.S. national average annual salary by experience level.

$45,000

Entry-level

$65,000

Mid-level

$90,000

Senior-level

Estimated figures for planning purposes — actual pay varies by location, employer, and experience.

Tools you'll master

Prompt engineeringLabeling platformsEvaluation rubricsLLM fundamentals

Practical training, every chapter

  • A live "try it yourself" example in every chapter you can run immediately
  • A hands-on exercise per chapter, with hints and a reveal-able answer
  • An open-ended challenge per chapter, graded against real success criteria
  • Class practice prompts for pairs or cohorts, not just solo homework

Why take this program

Interview- and job-ready

Built to the same bar Aicloud actually hires against — not a generic syllabus.

Tier 1 company partners

Program partnerships with leading employers are in progress — see Affiliation for status.

Job connection access

Graduates get access to Aicloud's own hiring pipeline and employer referral network.

Professional networking

Cohort discussion boards and live sessions connect you with peers and mentors, not just content.

A real portfolio

You leave with a concrete, explainable artifact — proof of knowledge, not just a certificate.

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