What the 30-Day Reskilling Playbook actually produces

# Week 1: Foundation in AI Tools & Prompt Engineering for Recruiting **Theme: Hands-on AI intro + translating your Boolean search expertise into prompt engineering** **Day 1: Map Your AI Toolkit & Set Up Free Accounts** - Action: Create free accounts on ChatGPT (free tier), HireEZ free trial, and bookmark Beamery docs - Deliverable: Screenshot folder with logins + a 5-bullet list of what each tool claims to do for recruitment - Time: ~20 min (setup) + 10 min (reading feature summaries) **Day 2: ChatGPT Fundamentals for Recruiters** - Action: Follow DeepLearning.AI's "Short Courses" → **ChatGPT Prompt Engineering for Developers** (20 min video walkthrough) - Deliverable: Screenshot your first 3 prompts (one asking ChatGPT to write a Boolean search string, one to draft a job spec, one to suggest screening questions) - Time: 25 min video + 20 min practice prompts **Day 3: Prompt Refinement & Iterative Improvement** - Action: Take one of yesterday's ChatGPT outputs (Boolean search or screening questions) and iterate 3 times, improving the prompt each time based on output quality - Deliverable: Document showing "Prompt v1 → Output" → "Prompt v2 → Output" → "Prompt v3 → Output" (Google Doc or Markdown file) - Time: ~30 min **Day 4: Replicate Your Current Sourcing Workflow in ChatGPT** - Action: Write ChatGPT prompts that mimic your current work: (a) parse a job description into candidate profile, (b) draft outreach email, (c) list Boolean operators to find niche skill combos - Deliverable: 3-5 reusable prompts saved in a plain-text file (you'll build on these all month) - Time: ~25 min prompting + 15 min organizing **Day 5: Explore HireEZ Free Trial – Hands-On Sourcing** - Action: Load a real (or sample) job description into HireEZ; use their AI-powered candidate suggestions to source 5 prospects; compare results to your manual Boolean search - Deliverable: Side-by-side comparison: "Manual Boolean Results vs. HireEZ AI Results" (screenshot + brief notes on matching quality, time saved, false positives) - Time: ~35 min **Day 6: Beamery Docs Deep-Dive & Architecture Skim** - Action: Watch Beamery's official product tour (YouTube, ~12 min) + skim their "AI-Powered Talent Intelligence" docs - Deliverable: One-page diagram or outline of "Beamery's predictive workflow: [input] → [AI module] → [output]" - Time: ~25 min video + 10 min diagram **Day 7: CHECKPOINT – Foundation Review & First Synthesis** - Action: Write a 200-word reflection comparing ChatGPT, HireEZ, and Beamery on one use case (e.g., "sourcing senior engineers in your industry"). Which tool felt most aligned with your sourcing gaps? What surprised you? - Deliverable: Reflective writing + decision matrix (tool name × ease of use, relevance to your role, learning curve) - Checkpoint: Have you written working prompts? Have you used a real AI tool for sourcing? If yes to both, you're on track.

# Week 2: Deep Dive into Predictive Hiring & Workforce Analytics **Theme: Shift from tactical sourcing → strategic prediction; learn how AI pipelines forecast hiring outcomes** **Day 8: Predict Candidate Success – YouTube + ChatGPT** - Action: Watch **"Predictive Analytics in HR" by DataCamp (free YouTube, 18 min)** + ask ChatGPT: "What data points do you need to predict offer acceptance rate? What about time-to-productivity?" - Deliverable: Worksheet titled "Predictive Hiring Model Inputs" — list 10–12 data points (e.g., past offer acceptance %, skill match score, interview-stage conversion %) that feed a prediction - Time: ~20 min video + 15 min ChatGPT dialogue **Day 9: Build Your First Hiring Metrics Dashboard Mockup (Google Sheets)** - Action: Create a Google Sheets template that tracks: candidate sourcing velocity, conversion rates by stage, time-to-fill by role, offer-acceptance rate trends. Use sample data (fabricate if needed). Add one formula (e.g., =AVERAGE or =PERCENTILE for benchmarking). - Deliverable: Published Google Sheets link (anyone with link can view) showing a 3-month trend with at least 2 KPIs and 1 calculated benchmark - Time: ~35 min **Day 10: AI for Predictive Pipeline Forecasting** - Action: Prompt ChatGPT: "I have [X candidates] in pipeline, [Y% advance] at each stage. If I close 3 roles in 60 days, what probability do I need per stage to succeed?" Have ChatGPT help you build a simple forecast model (text-based; no code needed). - Deliverable: ChatGPT conversation screenshot + a one-page written forecast for a real or hypothetical 3-role hiring plan - Time: ~25 min **Day 11: Explore Workforce Planning Use Cases – YouTube Playlist** - Action: Watch **Greenhouse's "Recruiting Metrics That Matter" webinar (YouTube, ~30 min)** and take 2–3 notes per section on how metrics drive hiring strategy - Deliverable: 1-page summary: "How Our Hiring Metrics Could Inform Workforce Planning" (connect your current KPIs to forward-looking decisions, e.g., "If offer acceptance drops, plan for larger pipeline") - Time: ~35 min (video + notes) **Day 12: Map Your Company's Hiring Data – Interview Notes** - Action: If possible, interview your current hiring manager or ops person (15 min): What hiring metrics does your company track? Where are the pain points (e.g., slow sourcing, low-quality candidates, unpredictable offer acceptance)? Document in a simple table. - Deliverable: Pain-points table with 4–5 rows: [Pain Point] | [Current Process] | [Where AI Could Help] | [Tool/Approach] - Time: ~30 min (15 min interview + 15 min documentation) **Day 13: HireEZ's Predictive Matching – Deep Dive** - Action: Spend a session in HireEZ testing their "candidate scoring" or "fit prediction" feature on a real job. Note: What data drives the score? How does it differ from your intuition? - Deliverable: Comparison of HireEZ's top 5 candidates vs. your top 5 (for the same role). Annotate: "Agree / Disagree" and why. Did HireEZ catch someone you missed? - Time: ~30 min **Day 14: CHECKPOINT – Build Your First AI-Augmented Hiring Brief** - Action: Select one open role (real or realistic). Draft a "Hiring Brief 2.0" using: (a) ChatGPT-generated candidate persona, (b) HireEZ's suggested sourcing channels, (c) your metrics-based pipeline forecast (from Day 9), and (d) predicted offer-acceptance probability. Format as a 1-page brief to a hiring manager. - Deliverable: Published 1-page Hiring Brief (Google Doc) ready to share with leadership. Include: role, AI-generated persona, sourcing plan, pipeline forecast, predicted success metrics. - Checkpoint: Does the brief feel more data-driven and strategic than your old job specs? Can you imagine iterating this with your hiring managers weekly?

# Week 3: Build & Ship Your Predictive Sourcing & Analytics Portfolio Piece **Theme: Move from learning to building; ship a real, public deliverable** **Day 15: Design Your Portfolio Project – Scope & Spec** - Action: Decide on your artifact (see Portfolio Artifact below). Map: What will it show? Who will view it? What skills will it demonstrate? Write a 1-page project brief. - Deliverable: Project brief including: name, goal, key screenshots/outputs, where you'll host it (GitHub + public Google Sheets or Notion template), estimated completion date (Day 28) - Time: ~20 min **Day 16: Set Up GitHub Repo + Document Your Approach** - Action: Create a public GitHub repo (if you don't have one, follow GitHub's free account setup, ~5 min). Add a README.md that explains: the problem you're solving, the AI tools/prompts you're using, the data you'll track, and how someone could replicate or adapt your work. - Deliverable: Public GitHub repo link with a well-written README (at least 150 words). Include a rough outline of what you'll add by Day 28. - Time: ~25 min **Day 17: Prototype Your Sourcing Automation Workflow** - Action: Build a reusable ChatGPT prompt + HireEZ workflow that automates candidate sourcing for a specific role type. Test it end-to-end: job spec → prompt → candidate list → scoring → outreach draft. - Deliverable: Screenshot sequence showing the full workflow (4–5 screenshots) + a Google Sheet with 10 sourced candidates scored by fit (even if manually scored; the process matters more than the data quality now) - Time: ~40 min **Day 18: Create a Prompt Library for Recruiting Tasks** - Action: Build a structured Google Sheet listing 15–20 reusable ChatGPT prompts organized by recruiting function (sourcing, screening, outreach, pipeline management, metrics analysis). For each, include: prompt name, the exact prompt text, expected output, and one example output. - Deliverable: Public Google Sheet (anyone with link) titled "AI Recruiting Prompt Library" with all 15–20 prompts, examples, and usage notes - Time: ~45 min **Day 19: Integrate Predictive Scoring into Your Pipeline** - Action: Take your Day 9 metrics dashboard and add a "Predictive Success Score" column or section. Use simple logic (which ChatGPT can help you write as a formula): e.g., a candidate's fit score (0–100) combined with your historical conversion rates to predict probability of hire. - Deliverable: Updated Google Sheets dashboard with a new "Predictive Scoring" section showing at least 3 candidate profiles scored and ranked by predicted hire probability - Time: ~30 min **Day 20: Documentation & Walkthrough Video (Optional Asynchronous)** - Action: Record a 5–7 minute screen-share walkthrough of your portfolio piece (use Loom free tier). Explain: the problem, your AI tool choices, how it works, and what hiring outcome it improves. - Deliverable: Loom video link (public) embedded in your GitHub README or shared in a summary doc - Time: ~20 min recording + 10 min editing notes **Day 21: CHECKPOINT – Public Ship & First Feedback Cycle** - Action: Ship your portfolio piece publicly. Post on LinkedIn (tag your network, ask for feedback), share in relevant Slack communities (e.g., "AI in Recruiting" Slack groups, r/recruiting), and message 2–3 senior recruiters asking for 5-min feedback on usefulness. - Deliverable: (a) LinkedIn post with link to project, (b) 2–3 feedback responses captured, (c) brief reflection on what you'd improve based on feedback - Checkpoint: Is your work visible and credible enough that someone who sees it thinks "this person understands AI + recruiting"? If yes, keep building. If feedback suggests confusion or missing pieces, allocate Day 22 to refine.

# Week 4: Deepen Expertise & Prepare for Interviews **Theme: Polish your portfolio, document your learning, and position yourself as an AI-augmented talent strategist** **Day 22: Refine Portfolio Based on Feedback** - Action: If you received feedback on Day 21, spend this day improving the clarity, accuracy, or scope of your project. If feedback was minimal, add a "Lessons Learned" section to your README and polish documentation. - Deliverable: Updated GitHub README with a "Key Learnings" or "What I'd Do Next" section; updated dashboard/prompt library with any refinements - Time: ~30 min **Day 23: Build a Case Study – "AI Sourcing in Action"** - Action: Document a real or realistic hiring scenario: "I used ChatGPT + HireEZ + my metrics to source for a [specific role]. Here's what happened." Write as a 1–2 page case study: the challenge, your AI-driven approach, the results (candidates sourced, time saved, quality improvement). - Deliverable: Public 1–2 page case study (Google Doc or Medium post) with before/after metrics and learnings - Time: ~35 min **Day 24: Strategic Workforce Planning Module – Final Deep Dive** - Action: Watch **LinkedIn Learning's "Workforce Planning" free overview** and/or skim **SHRM Workforce Planning guide** (free PDFs). Synthesize: how would you use your new AI tools to advise a CEO on headcount forecasting or skill-gap planning? - Deliverable: 1-page memo titled "How AI Tools Can Improve Workforce Planning" – pitch it as if presenting to a C-level executive - Time: ~35 min **Day 25: Competitive Landscape Research** - Action: Research 3–5 competitors or peers who are also doing "AI-augmented recruiting." Read their blog posts, listen to podcasts, or skim their websites. What are they doing? Where are they ahead? Where can you differentiate? - Deliverable: 1-page "Competitive Landscape" summary identifying your unique angle or value-add (e.g., "I'm focused on offer-acceptance prediction" or "I'm democratizing AI sourcing for mid-market companies") - Time: ~30 min **Day 26: Interview Preparation – Story & Talking Points** - Action: Write 3 elevator pitches: (a) "Tell me about your transition to AI-augmented recruiting," (b) "Walk me through how you'd set up a predictive hiring pipeline," (c) "Why does AI matter in recruiting?" Practice each out loud (2 min per pitch). - Deliverable: Typed versions of all 3 pitches (Google Doc) + voice memos of you practicing (Loom or phone recording, private) - Time: ~25 min writing + 15 min practice **Day 27: Portfolio Polish & Presentation Package** - Action: Create a final portfolio package: a one-page PDF or Notion page that links to all your work (GitHub repo, Google Sheets, LinkedIn posts, case study, Loom video). Add a personal "About" blurb (150 words) explaining your recruiting background and your new AI focus. - Deliverable: Public portfolio page (Notion or simple website) or PDF ready to email. Should take <1 min to understand the breadth of your work and your vision. - Time: ~40 min **Day 28: CHECKPOINT – Submit Your Portfolio & Self-Assessment** - Action: Email or DM your portfolio package to 5 people in your network (recruiters, hiring managers, or AI practitioners). Ask for a 30-min coffee chat to discuss your transition. Simultaneously, write a 300-word self-assessment: your growth areas (e.g., "predictive modeling," "AI workflow design"), your confidence level on each AI tool, and your next learning priorities. - Deliverable: (a) Portfolio sent to 5 people (with log of who + dates), (b) 300-word self-assessment saved - Checkpoint: You now have a public body of work, introductions in flight, and clarity on your strengths and gaps. This is your springboard to your next role. **Day 29: Rest / Consolidation** - Action: Light day. Review your Month 1 portfolio and learning. Organize your GitHub repos, Google Sheets, and notes. Read 1 article on "Future of AI in Recruiting" (e.g., from HR Tech Today, LinkedIn Talent Blog, or HireEZ blog). - Optional: Attend 1 recruiting/AI meetup or webinar. - Time: ~20 min reading **Day 30: Reflect, Document, & Plan Month 2** - Action: Write a final reflection (500 words): What surprised you? What's your biggest insight about AI in recruiting? What's the one thing you want to master next (e.g., "predictive modeling," "Beamery workflows," "gen-AI fine-tuning")? Set a 30-day learning goal for Month 2. - Deliverable: Reflection document + a 3-goal roadmap for Month 2 (e.g., Goal 1: Ship an advanced predictive model; Goal 2: Land a conversation with an AI recruiting vendor; Goal 3: Publish a public blog post) - Time: ~40 min