Day 1: Foundations – Introducing AI, Automation, AI Agents, and the Job Search Process
Day 1 builds essential foundational knowledge by introducing core AI concepts, agentic systems, and structured job-search methodologies so participants can immediately see how AI transforms a traditionally chaotic process into a strategic, repeatable system.
Segment 1: Introduction to Artificial Intelligence in Job Search
This segment delivers a high-level overview of generative AI’s evolution and its proven impact on hiring, combined with a live basic-prompting demo that lets every participant experience instant value.
- Overview of AI vs. traditional job-search tools
- Evolution and capabilities of generative AI (GenAI)
- Real-world statistics (87 % of companies now use AI in hiring)
- Live demo: crafting effective self-assessment prompts
Segment 2: Concepts of Automation and AI Agents
This segment clearly differentiates rule-based automation from intelligent AI agents and demonstrates how agentic workflows can autonomously handle multi-step job-search tasks.
- Rule-based automation vs. perception-reasoning-action AI agents
- Single-agent vs. multi-agent systems (e.g., CrewAI-style or no-code Zapier/Make.com)
- Real job-search agent examples (scrape alerts → analyze JD → draft outreach)
- Live demo: building a simple custom GPT or conversational agent
- Ethical considerations and current limitations
Segment 3: The Job Search Process and Methodologies
This segment contrasts traditional “spray-and-pray” approaches with AI-enhanced, goal-aligned frameworks and maps a clear phased execution model.
- Traditional vs. targeted networking methodologies
- AI-enhanced framework: goal alignment → skills mapping → phased execution
- Step-by-step process from Meta’s Job Search with GenAI
- Common pitfalls and how AI eliminates them
- Group discussion on personal application
Segment 4: Interactive Exercise & Wrap
This closing segment lets participants apply the day’s concepts immediately and prepares them for the next day’s hands-on work.
- AI-powered transferable-skills discovery exercise
- Drafting a one-paragraph career identity statement
- Q&A and targeted homework assignment
Day 2: AI-Powered Personal Branding and Beating Recruiter AI – CVs/Resumes, Recruiter Expectations, and Shortlisting
Day 2 focuses on creating recruiter-ready personal branding materials and teaching participants exactly how to optimize every document to survive and thrive inside AI shortlisting systems used by 87 % of employers in 2026.
Segment 1: AI-Made CVs and Resumes
This segment provides an end-to-end, prompt-engineered workflow for building or optimizing ATS-friendly resumes and cover letters in minutes.
- Prompt engineering for every resume section (objective, achievements, education)
- ATS-friendly formatting rules and keyword strategies
- Live hands-on tailoring using real job descriptions
- Tool comparison (ChatGPT, Claude, Gemini) and best practices from Udemy & LinkedIn Learning
- Ready-to-use templates and prompt library
Segment 2: What Recruiters Want
This segment distills current recruiter priorities and decision-making criteria so participants can align their documents with what actually moves the needle.
- Quantifiable achievements over duties
- Keyword relevance combined with genuine context
- Cultural-fit signals and brevity (1–2 pages)
- Clean design and storytelling principles
- Synthesized insights from recruiter panels in analyzed courses
Segment 3: AI Shortlistings by Recruiters and Optimization Strategies
This segment demystifies how modern ATS and semantic-matching AI systems work and equips participants with precise tactics to beat them.
- How semantic matching (not just keywords) functions in 2026
- Key statistics: 75 % of resumes never reach humans, 6–7 second initial scan
- Optimization techniques: mirroring language, natural phrasing, structure for parsing
- Live demo: analyzing a JD and optimizing a resume segment
- Free ATS-scanner testing method
Segment 4: Practice, Peer Feedback & Wrap
- Live tailoring exercise on personal documents
- Structured peer-feedback protocol
- Full optimization of one target resume/cover letter (homework)
Day 3: Scaling Success – Automated Job Search, Applications, Metrics, and Advanced Strategies
Day 3 shifts from document creation to high-volume, automated execution, teaching participants how to run a metrics-driven, AI-augmented campaign that consistently generates more interviews with less manual effort.
Segment 1: Automated Job Search
This segment reveals how to discover both visible and hidden opportunities using semantic alerts and intelligent research agents.
- AI-powered job aggregators and semantic LinkedIn/Indeed alerts
- Hidden job-market discovery techniques
- Automated company and culture-fit research
- Ethical boundaries when using automation
Segment 2: Automated Applications, Scaling, and Numbers
This segment shows exactly how to scale personalized applications safely while tracking the numbers that matter for success in 2026.
- Realistic benchmarks (32–200+ applications needed for one offer)
- No-code automation workflows (Sheets + AI trackers)
- Bulk personalization without spamming
- Key metrics: response rate, time-to-interview, A/B testing
- Google Coursera-inspired application tracker template
Segment 3: Advanced Integration – LinkedIn, Networking, Interviews, and Beyond
This segment integrates AI across the full candidate journey, from profile optimization to final negotiation.
- LinkedIn profile, posts, and outreach automation
- Networking message generation and interview prep with Gemini Live
- STAR-method mock sessions and common-question libraries
- Post-interview thank-yous and negotiation scripts
- Emerging 2026 trends in recruiter-side AI agents
Segment 4: Workshop Wrap-Up and Personal Action Plan
This final segment consolidates all three days into a concrete 30-day execution plan and provides ongoing resources.
- Creation of a personalized 30-day AI-powered job-search plan
- Group reflections and resource sharing (full prompt library, tool list)
- Certificate of completion and post-workshop support details
