
The role of AI in SMART goal setting and OKRs
Automating the goal-setting process
Tools:
Betterworks: generating quality goals
Marissa AI: automatic generation of goals and key results
Sloneek: generation of OKRs and evaluation questionnaires
Tools in this lecture
Ready to use on the slides (1)
Services and apps shown in this lecture (4)
Models and methods explained (1)
Using AI for real-time performance monitoring
Predictive analytics and trend detection
Tools:
Mesh (Nudges): Automating actions to improve performance
Intelogos: smart recommendations and reports to improve team performance
Skilla.ai: automatic feedback generation and productivity analysis
Tools in this lecture
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Models and methods explained (1)
Creating questions for AI review forms
Preparing data for objective employee evaluations
Tools:
Effy AI: free software for performance management with AI
15Five: synthesize feedback and support for conducting interviews
Sloneek: generating appraisal questionnaires based on skills data
Tools in this lecture
Services and apps shown in this lecture (3)
Models and methods explained (1)
Optimizing the process of one-on-one meetings
Automate the analysis of review outcomes
Tools:
Lattice: performance insights and development recommendations
Peoplebox: AI for meeting and retaining top employees
Marissa AI: personalized feedback and personal development plans
Tools in this lecture
Services and apps shown in this lecture (3)
Customizing data-driven development plans
Using AI to build career tracks
Tools:
Marissa AI: generating personalized development plans
15Five: development and performance management recommendations
Betterworks: development plans based on performance data
Tools in this lecture
Ready to use on the slides (4)
Integration of AI tools into existing HR systems
Automation of performance management cycles
Metrics and KPIs to assess the impact of AI
Tools:
Intelogos: integrate with HR processes and generate analytics
Skilla.ai: compensation and performance management with AI
Peoplebox: a complete solution for talent management with AI
Tools in this lecture
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Models and methods explained (2)
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The agentic loop: stop_reason "tool_use" versus "end_turn" and tool results in history
Loop anti-patterns: parsing text for completion and iteration caps as the main stop
Hub-and-spoke coordinator, isolated subagent context and dynamic subagent selection
Task tool, AgentDefinition and parallel subagents from one coordinator response
Tools: agent architecture blueprint, exam trainer on Domain 1 (27%), flashcards, memory and match games
Tools in this lecture
Files to download (2)
Ready to use on the slides (11)
Models and methods explained (2)
Programmatic prerequisites and hooks versus prompt guidance when a rule must hold every time
PostToolUse data normalization and tool call interception that blocks policy-violating actions
Structured handoff to a human agent, prompt chaining versus dynamic decomposition
Sessions on purpose: --resume by name, fork_session or a fresh start with a summary
Tools: guardrail spec, handoff template, exam trainer on Domain 1 and study games
Tools in this lecture
Files to download (2)
Ready to use on the slides (14)
Models and methods explained (3)
Tool descriptions that separate similar tools: inputs, outputs, edge cases, when not to use
Structured MCP errors: isError, error category, retryable flag, empty result versus access failure
A small tool set per agent, scoped cross-role tools and tool_choice auto, any and forced
.mcp.json with environment variables, ~/.claude.json, MCP resources and the built-in tools
Tools: tool contract and structured error kit, exam trainer on Domain 2 (18%) and study games
Tools in this lecture
Files to download (2)
Ready to use on the slides (12)
CLAUDE.md hierarchy at user, project and directory level, @import and /memory
.claude/rules/ with YAML paths globs for conventions spread across the codebase
Slash commands and skills: SKILL.md frontmatter with context: fork, allowed-tools, argument-hint
Skill or CLAUDE.md: on-demand workflows versus always-loaded standards
Tools: Claude Code team config pack, exam trainer on Domain 3 (20%) and study games
Tools in this lecture
Files to download (2)
Ready to use on the slides (10)
Models and methods explained (1)
Plan mode or direct execution, and the Explore subagent for verbose discovery
Iterative refinement: input and output examples, test-driven iteration, the interview pattern
Claude Code in CI: -p, --output-format json and --json-schema for inline PR comments
An independent review instance, prior findings in context and existing tests for test generation
Tools: plan-or-execute card, CI review pipeline spec, exam trainer on Domain 3 and study games
Tools in this lecture
Files to download (2)
Ready to use on the slides (13)
Models and methods explained (1)
Explicit review criteria and severity examples that cut false positives
Few-shot examples for ambiguous cases and varied document formats
Tool use with JSON schemas: nullable fields, "unclear" and "other" enums, tool_choice
Validation-retry with error feedback, the Message Batches API and multi-pass review
Tools: extraction schema and review prompt kit, exam trainer on Domain 4 (20%) and study games
Tools in this lecture
Files to download (2)
Ready to use on the slides (16)
Models and methods explained (1)
Case facts block, trimmed tool output and the lost-in-the-middle effect
Escalation triggers, explicit requests for a human and multiple customer matches
Error propagation with partial results, scratchpad files, /compact and state manifests
Human review by stratified sampling, claim-source mapping and conflicting sources
Tools: context and escalation playbook, exam trainer on Domain 5 (15%) and study games
Tools in this lecture
Files to download (2)
Ready to use on the slides (18)
Models and methods explained (1)
Five domains and their weights, the six exam scenarios and why you get four of them
Multiple-choice and multiple-response items and the "most effective first step" logic
Readiness score by domain weight against the scaled pass mark of 720
Full mock exam: 60 questions in four scenario sets, 120-minute timer, flags, review screen, score by domain
Tools: readiness scorecard and the 60-question mock exam
Tools in this lecture
Files to download (2)
Ready to use on the slides (11)
From a business problem to a Claude solution tied to value: efficiency, cost and performance SLAs
End-to-end architecture: input, processing, output and a feedback loop
Workflow, agentic or augmented LLM pattern, chosen on purpose
Multi-agent orchestration and decomposition of complex problems
Tools: solution design canvas, exam trainer on Domain 1 (17%), flashcards, memory and match games
Tools in this lecture
Files to download (2)
Ready to use on the slides (9)
Models and methods explained (10)
Claude model choice for the task by quality, cost and latency
System prompts, templates and guardrails
Zero-shot, few-shot and chain-of-thought: which technique fits which task
Context window budget, token usage and prompt reuse with caching, modular prompts and Skills
Tools: model and prompt spec, exam trainer on Domain 2 (13%) and study games
Tools in this lecture
Files to download (2)
Ready to use on the slides (23)
Models and methods explained (9)
Capability bloat: which tools and agents to remove under least privilege
Authentication and authorization gaps and how to close them
Accuracy against latency: how to justify a configuration decision
MCP, API/CLI or agent-to-agent, and progressive discovery versus monolithic context
Tools: integration security review, exam trainer on Domain 3 (19%) and study games
Tools in this lecture
Files to download (2)
Ready to use on the slides (14)
Models and methods explained (8)
Chunking and indexing strategies for your documents
Retrieval matched to data shape and query pattern, reranking and citations
Stale or irrelevant chunks: the first suspect when answers go wrong after a refresh
Observability at scale: what to log and what to alert on
Tools: RAG and observability design, exam trainer on Domain 3 and study games
Tools in this lecture
Files to download (2)
Ready to use on the slides (17)
Models and methods explained (7)
Metrics for accuracy, latency, cost, safety and security, with thresholds
Evaluation datasets and test frameworks built with mixed methods and graders
A/B testing and iterative improvement
Diagnosing prompt failure, hallucinations and model mismatch; token, latency and cost tuning
Tools: eval plan and metrics sheet, exam trainer on Domain 4 (16%) and study games
Tools in this lecture
Files to download (2)
Ready to use on the slides (17)
Models and methods explained (6)
Risks, limitations and failure modes of LLM systems
Guardrails and safety controls placed where the risk is
Human-in-the-loop validation points
GDPR, HIPAA and FedRAMP needs mapped to architecture; bias, fairness and transparency
Tools: guardrail and compliance matrix, exam trainer on Domain 5 (14%) and study games
Tools in this lecture
Files to download (2)
Ready to use on the slides (18)
Models and methods explained (5)
Structured discovery and requirement gathering
Decision records, expectation alignment and service levels
Lifecycle from discovery to handoff, monitoring and iteration; documentation and a runbook
Claude Code for teams, AI-assisted developer workflows and operational debugging
Tools: decision record and handoff pack, exam trainer on Domains 6 (14%) and 7 (7%) and study games
Tools in this lecture
Files to download (2)
Ready to use on the slides (16)
Models and methods explained (3)
Seven domains and their weights, multiple-choice and multiple-response items
The logic of a Professional item: one defensible change in a system that is already failing
Readiness score by domain weight against the scaled pass mark of 720
Full mock exam: 63 questions, 120-minute timer, flags, review screen and a score report by domain
Tools: readiness scorecard and the 63-question mock exam
Tools in this lecture
Files to download (2)
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This course contains the use of artificial intelligence.
3,465 tools for AI-supported performance management in one course: 2,754 ready-to-use files, prompts, forms, rubrics and checklists, 107 AI services and apps shown on screen, plus 604 models and methods.
Open a lecture, take the tool, use it in this cycle's goals, reviews or development plans.
Why AI has not made reviews easier yet
Everyone has tried asking a chatbot to write a review. The result sounds fluent and says nothing, because the model was given no evidence, no criteria and no format. So managers go back to writing by hand, HR goes back to chasing forms, and AI stays a toy. The gain is real, but it comes from structure: the right prompt, fed with the right data, producing a document in a format you already use.
What changes after this course
You have that structure ready. Prompts that draft a development plan from a role and a gap. Prompts that summarise review evidence instead of inventing it. Forms and rubrics that give the output a shape. Checkers that catch a weak review before it is sent. Worksheets that tell you which HR tasks are worth handing to AI at all, and how to connect the data safely.
The highlight: exam trainers, study games and mock exams
The Claude certification sections come with interactive files you open in a browser and play:
What is inside: 3,465 tools, counted file by file
You do not have to take this number on trust. Open any lecture and read its description: every tool inside is listed there by name.
Where the tools are
Who is teaching
I am Mike, the #1 HR instructor on Udemy, with 1,800,000+ course enrollments from 170,000 students, and the author of about 200 books on Amazon. I built the people system at Preply as it became a unicorn and worked at Wargaming, iDeals and Alfa-Bank across 20 years in HR. I hold PHRi, SHRM-CP and HCI sHRBP certifications and represent HRCI in Eastern Europe.
My performance management experience includes building a complete system at Preply and implementing another from scratch at iDeals. At Preply, I introduced 15Five, OKRs and regular check-ins as part of that system. Based on materials I have seen and feedback I have heard, the company continued using it afterward. These implementations provide the performance management foundation for the tools we explore in this course, including applications of AI.
How to use the course
Watch the first five sections to see AI at work across the performance cycle. Then use the rest as a reference library: section and lecture titles show how many tools are inside, and each lecture description lists them by name, so you can go straight to the prompt or form you need.
What is included
Where to start
Take one review you have to write this month. Open the development plan lecture, use the prompt with real evidence about that person, and compare the draft with what you would have written. HR teams that give AI structure get hours back every cycle, and every review written from a blank page is an hour you will spend again next time. Enrol now and start your first lesson today.