How HelloPM Program Works - HelloPM AI Product Management Cohort

Main ideas & lessons

• HelloPM (Hello PM) is a structured program designed to help learners become confident product professionals, including people trying to break into product management.
• The program is an end-to-end journey in three phases:
  1. Learn product management (and AI product management).
  2. Practice through hands-on work, assignments, and building projects/products.
  3. Get career support for job readiness (resume, portfolio, interview prep, and job/network access).
• A core differentiator is that the curriculum is highly practical—focused on assignments, side projects, and “build your own product” rather than only theory.
• The program emphasizes long-term support after completion, including community access, office hours, and the ability to repeat modules/cohorts.

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Methodology / instructional structure

1) Three-step program structure

Step 1: Learning

• Covers product management fundamentals and an AI product management track.
• Includes case studies and tools across the PM lifecycle.

Step 2: Practice

• 80% of time is dedicated to practice activities.
• Includes assignments, side projects, and a dedicated period to build AI products.
• Projects built during the program become part of a portfolio.

Step 3: Career support

• Resume improvement via 1:1 calls.
• Portfolio curation and feedback.
• Interview preparation, including mock interviews.
• Access to an internal job board and network.
• Support is guidance-based, not guaranteed placements.

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2) Curriculum overview (weeks/modules as described)

Weeks 1–3: Foundations → Strategy/Execution

Week 1 (Learning PM fundamentals)

• Modern PM role introduction
• Product management lifecycle: Discovery, Delivery, Distribution
• How to use AI as a PM without sacrificing product sense
• AI tool glimpses across the PM cycle
• Practical case studies + examples

Weeks 2–3

• Product strategy creation
• Stakeholder management using “creative execution”
• Build product vision and derive strategy using:
  • Product vision building blocks
  • Customer insights
• Create roadmaps aligned to product strategy
• Detailed PRD (Product Requirements Document) creation
• Execution frameworks:
  • OKRs
  • Sprints
  • Running agile teams
• Case studies mentioned:
  • TikTok, Perplexity, Amazon Prime, Windsurf

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Discovery & Analytics (continuation)

Product discovery

• User and market research
• Identify impactful customer/user problems
• Convert research into:
  • User personas
  • Customer journey maps
• Identify, document, and pursue the right opportunities
• Frameworks mentioned:
  • Jobs To Be Done (JTBD)
  • “fivey moms test” (text unclear; likely a named framework)
• Case studies mentioned:
  • Lovable, MakeMyTrip, Stripe

Product analytics

• Data-driven decision-making
• Business metrics across B2B, B2C, SaaS, and more
• Define North Star metric
• Event-based tracking (events + properties)
• Funnel segmentation and cohort reports
• A/B testing and experiments
• Use AI for data cleaning
• Integrations mentioned:
  • Mixpanel, PostHog with GPT or Gemini
• Emphasis: hands-on session + case studies

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Weeks 6–11: AI product management deep dive

Week 6: Generative AI foundations

• Generative AI building blocks
• Comparison: predictive/old AI vs modern/generative AI
• How LLMs work (GPT/Claude/Gemini mentioned)
• Concepts described:
  • Pre-training and where data comes from
  • Neural networks
  • Post-training / alignment
  • Turning LLMs into assistants/use cases
• AI economy + AI product infrastructure
• Attention mechanism (why progress occurred)
• AI product discovery: finding AI use cases for customers
• AIPM stack: a seven-step framework to implement AI / create AI product strategy
• Case studies:
  • ChatGPT, “Entropic” (likely Anthropic), Netflix

Week 7: System architecture of AI products

• Promise: even non-technical backgrounds can understand
• Components:
  • data, models, context, tools, feedback loops
• Contextualizing LLMs for the product/use case:
  • Fine-tuning
  • Prompt engineering
  • RAG (retrieval-augmented generation)
  • When to use which contextualization approach
• Examples:
  • Stripe, Notion AI, ChatGPT, Salesforce Einstein

Evaluations (after architecture, then deep dive)

• Events + how to write them
• Evaluation characteristics (as described)
• Handling:
  • Non-determinism
  • Hallucinations
• Use evaluations to improve/grow AI products
• Examples mentioned (names appear unclear in subtitles):
  • GMA, Granola, Gong

AI agents (next module after evaluations)

• What AI agents are and when to use them
• AI agent stack and a seven-step framework for building agents
• Model Context Protocol (MCP) mentioned as enabling agents
• Improving AI products with agents
• Building/deploying agents using platforms/tools named as unclear in subtitles:
  • Platforms mentioned as N/A10 (unclear)
  • Tools: NAN, Agent AI, ZPR (subtitles unclear)

Week 10: Designing AI product experiences

• Best practices and nuances for AI UX
• AI-native challenges:
  • Hallucinations
  • Streaming
  • Latency
  • Non-deterministic behavior
• Regulations, trust, safety, security
• Enterprise adoption
• Feedback loops and model choices
• Tools for quick AI interfaces:
  • Streamlit, Gradio
• Examples/tools mentioned:
  • Glean, Perplexity, Gradio (some duplication/unclear text)

Week 11: Market adoption of AI products

• Data feedback loops + platform thinking
• Product-led growth for AI:
  • B2C angle
  • B2B angle
• Scaling challenges
• Scale beyond prototype
• Case studies mentioned:
  • Cursor, Lovable, Grammarly, Notion (plus “and more”)

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Final: “10x’ing PM outcomes with AI”

• Use AI tools to become more productive
• Guidance on:
  • When to use AI
  • When not to use AI
• Using AI to build a PM portfolio
• AI tools to improve:
  • stakeholder management
  • communication
  • working with people
• Build your own AI tools to improve PM workflows

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3) Advanced modules (after initial modules)

Advanced Module 1: PM interview cracking + growth planning

• Interview question types
• Frameworks by seniority and company context
• Mock interviews:
  • Start with two
  • Unlock more after applying mentor feedback
• Growth planning for product:
  • Growth definition
  • User acquisition and product marketing
  • Product-led growth
  • Growth loops
  • “ARR funnel” optimization:
    • usage, engagement, retention, acquisition, revenue

Complimentary course: “Technology for product managers”

• Delivered in a 6-week complimentary format
• Topics:
  • APIs, cloud computing, webhooks
  • SQL basics
  • Working with engineers
  • Git/GitHub
  • System design concepts
• Can be taken alongside or after the main program

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4) Tooling (categories mentioned)

• Design / wireframes: Figma
• Analytics: Mixpanel, PostHog, Google Analytics 4
• Project management: Jira, Asana
• Misc AI tools: ChatGPT, “Granula” (likely Grammarly/Granula typo), “cloud desktop” (unclear)
• References integrating tools using “MCPS” (unclear acronym)

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Practice & portfolio emphasis

• 80% of program time is described as practice.
• A final “build your own AI products” block reserves the last 3 weeks for building.
• Learners can:
  • bring their own problem statement, or
  • receive one from the program
• Outputs are intended to become a portfolio demonstrating credibility.

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Career support details (four-part support)

1. Resume improvement
   • Expert runs a 1:1 call to tailor resume for better shortlisting.
2. Portfolio creation
   • Review + feedback on assignments/projects
   • Curation of work into a portfolio, reviewed by the HelloPM team
3. Interview preparation
   • Guidance on articulating answers
   • Mock interviews:
     • start with two
     • unlock more via iterative feedback
4. Job / network support
   • Internal job board updated frequently (nearly weekly)
   • Some jobs may include referrals/introductions
   • Disclaimer: no placement guarantee and no guaranteed number of referrals

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Delivery, scheduling, and logistics (FAQ-style)

• Application / onboarding
  • Apply via website; payment then onboarding within a few hours (as stated)
• Cohorts / groups
  • Weekend batches for working professionals
  • Split into groups of 3–5 learners with a mentor for interactivity
• Attendance / recordings
  • Online live curriculum
  • Recordings + notes/slides provided if missed
  • If missing many sessions, advised to move to the next cohort for catch-up
• Office hours
  • Thursday morning and evening “office ask” sessions for questions
  • Office hours continue even after the program ends to clear doubts
• Time commitment
  • ~15 hours/week:
    • 4–5 hours live sessions
    • 10–11 hours practice/assignments/building/revision/alumni success story review
• Work alongside full-time job
  • Yes—most learners are stated to be employed; assistance available if workload is difficult
• Support after completion
  • 2 years support: office hours, WhatsApp group, Slack group, offline events, repeat cohort without additional charges for two years
• Fast track option
  • Pre-access recordings of past cohorts for self-paced learning (~40–45 days)
  • Includes live sessions for the current batch
  • No extra cost mentioned for fast track
• Certification
  • Completion certification + separate certifications for bonus modules:
    • Technology for product managers
    • Growth for product managers
    • AI for product managers

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Differentiation claims

The program claims three main strengths:

1. Most detailed curriculum for product management + AI product management
2. Practical learning (assignments, side projects, build-your-own-product)
3. Long-term support (2 years post-completion; community + updated content)

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Speakers / sources featured (as mentioned in the subtitles)

• Speaker
  • The video appears narrated by a single HelloPM representative (name not provided in subtitles).
• Notable brands/companies cited in examples/case studies
  • Microsoft, Amazon, CRED, Freshworks, Walmart, TikTok, Perplexity, Amazon Prime, Windsurf, Lovable, MakeMyTrip, Stripe, ChatGPT, Anthropic (“Entropic”), Netflix, Grammarly, Notion, Salesforce, Cursor, Gong (plus other companies referenced in curriculum examples).
• Instructors / mentors
  • “Product leaders/product directors/VPs of product management” from companies including Walmart, Misho, Ambition Box, Meta, LinkedIn, IndiaMart, and others (individual names not provided in subtitles).