Imagine you’re a Product Manager at a fast-growing e-commerce company. Your customer support team is overwhelmed with repetitive queries: “Where is my order?”, “How do I return an item?”, “What’s your refund policy?”. Your team spends 60% of its time on these simple questions, leaving little room for complex, high-value customer issues. You’ve tried chatbots, but they often fail, escalating frustrated customers to human agents anyway. What if you could deploy something smarter? Something that didn’t just follow a script but could understand context, access order databases, initiate a return process, and even learn from interactions to get better over time?

That’s where the concept of AI Agents comes into play. You’re not just building another feature; you’re creating an autonomous team member. This is the new frontier for Product Managers in the AI era—moving from designing static software to orchestrating intelligent, autonomous systems. Understanding the AI Agents meaning and its potential is no longer optional; it’s central to building the next generation of products.

About The Course

Ready to step into the world of AI Product Management? We created this Free AI PM Course to be your perfect starting point. We’ll walk you through everything you need to know, from the basics of how Generative AI works to the way companies are building new products with it. You’ll get a clear, behind-the-scenes look at how technologies like LLMs (the brains behind tools like ChatGPT) actually function and learn the step-by-step lifecycle of an AI product. We break down the tricky tech topics like RAG and Prompt Engineering into simple terms, so you can feel confident working with engineering teams.

Our goal is simple: to give you the real-world skills you need to build a career in AI. This course is packed with practical examples from actual companies, showing you exactly how AI is used today. We’ll teach you how to spot good AI opportunities, build a project for your own portfolio, and even get you ready for AI PM interviews. By the end, you won’t just understand AI, you’ll have the confidence and the knowledge to build amazing products and take the next big step in your career. All the videos and materials are completely free when you finish. Come join us and start your journey!

Who Should Take This Course?

This course is ideal for:

  • Anyone who wants to become an AI Product Manager: Build your knowledge from the ground up and learn the specialized skills needed to enter this exciting field.
  • Aspiring Product Managers: Gain a competitive edge by mastering the principles of AI, setting yourself apart in the job market.
  • Product Managers who want to leverage AI: Transition your existing PM skills into the AI space and learn how to integrate cutting-edge technology into your product strategy.

This course covers:

  • Core AI use cases for businesses with software or digital experiences.
  • Best practices for building AI-powered features and functionality.
  • How AI can transform and elevate the product-led organization.
  • Applying AI tools and use cases to accelerate product value and business growth.

Session 1: Introduction to Generative AI & the LLM Economy

Get started with the foundations of AI Product Management. Learn what Generative AI is, how Large Language Models (LLMs) actually work, and explore the different types of AI Product Managers shaping today’s industry.

  • What is Generative AI and why it matters for Product Managers
  • The rise of the LLM economy: ChatGPT, Gemini, Claude, and more
  • Types of AI Product Managers (Tech PM, Data PM, Business PM, etc.)
  • How Large Language Models (LLMs) actually work (tokens, training, inference)

Slides 👇🏽

Figjam Notes

AI generated Summary of this Session: 👇

Please use caution with this AI generated summary. In a few cases, it may be inaccurate or misleading. Please report such instances to us through the ‘Help’ button on the bottom right of this page

What are you going to learn?

In this AI Sprint session, the instructor walks you through the fundamentals and applications of Artificial Intelligence, focusing especially on predictive AI, generative AI, and large language models (LLMs). You will learn how AI is transforming industries, the differences between predictive and generative approaches, the tools and frameworks that power today’s AI systems, and how you as a professional can leverage AI to build productivity, portfolios, and even products.

Why does it matter to you?

AI is no longer just a buzzword. It is actively reshaping how products are built, businesses are run, and careers are made. Understanding AI fundamentals — from data, models, and prompts to the way LLMs like ChatGPT actually function — equips you to harness its potential rather than being overwhelmed by it. For product managers, students, and professionals, this knowledge directly translates into employability, innovation opportunities, and the ability to stay ahead in an AI-first world.

Key takeaways:

  1. Predictive AI supports decisions by analyzing and labeling data.
  2. Generative AI not only understands but also transforms and creates new content.
  3. AI has applications across text, images, audio, video, and even biological data like DNA.
  4. Tools such as Whisper, MidJourney, and DeepMind’s AlphaFold showcase AI’s versatility.
  5. Large language models (LLMs) are trained using data, parameters, and loss functions to generate human-like responses.
  6. Frameworks like “Attention is All You Need” and transformers are the backbone of modern AI.
  7. To use AI effectively, focus on fundamentals: prompts, context, data, and continuous learning.

Introduction to the AI Sprint

The instructor begins by highlighting the importance of focus and discipline. Participants are reminded that investing time in learning AI requires full attention, especially since the content may feel overwhelming — like “drinking from a fire hose”. Over four sessions, learners are promised two outcomes:

  1. How to use AI to boost productivity.
  2. How to build projects or portfolios that can add career value.

The instructor sets expectations clearly: by following exercises, repeating the material, and reflecting on concepts, participants will gain the ability to apply AI practically.

Predictive AI

The session first introduces predictive AI. This type of AI supports decisions by fetching and labeling data, then making predictions. For example:

  • Predicting disease outcomes using patient records.
  • Supporting businesses in forecasting sales.
  • Using past behavior to make product recommendations.

Predictive AI doesn’t create new data; rather, it interprets existing data to guide future actions. The instructor stresses that not every problem needs generative AI; predictive models are often sufficient and more efficient.

Generative AI: The Game Changer

After 2022, with the release of models like GPT, the AI world expanded beyond prediction to creation. Generative AI, or “contextual data generation,” can:

  1. Understand content: comprehend prompts or inputs.
  2. Transform content: turn transcripts into summaries, or enhance images.
  3. Generate new content: create essays, images, or even fictional letters (e.g., “Mark Zuckerberg writing to Anthropic’s CEO”).

This shift is monumental because it allows AI to act as a collaborator, not just a calculator.

Applications of Generative AI

The instructor shares vivid examples across industries:

  • Coding: AI reduces costs for tech companies by generating and debugging code, traditionally one of the largest expenses.
  • Healthcare: AI can analyze DNA sequences to predict disease risks, as seen in Google DeepMind’s AlphaFold for protein folding.
  • Text: Summarizing long documents into digestible insights.
  • Images: Tools like MidJourney and Leonardo create infographics, designs, and art.
  • Audio: OpenAI’s Whisper transcribes audio into text; Sonos creates music; ElevenLabs produces realistic voices.
  • Video: Combining audio and images to generate ads or simulations at scale.

Together, these capabilities redefine creativity and productivity.

Generative vs Predictive AI

The instructor distinguishes the two clearly:

  • Predictive AI = supporting decisions with existing data.
  • Generative AI = creating content based on context.

Example: A bank might use predictive AI to forecast loan repayment risks, while generative AI could draft the actual loan agreement text.

Generative AI Value Stack

To avoid being overwhelmed, learners are given a framework — the Gen AI Value Stack. It’s described as a mental model for organizing how AI operates:

  1. Data foundation (text, image, audio).
  2. Models that understand and generate.
  3. Applications (assistants, simulations, decision aids).

This stack ensures you can classify any AI tool within a hierarchy, rather than seeing AI as an unmanageable black box.

How Large Language Models (LLMs) Work?

The session dives into the fundamentals of LLMs:

  • They are trained on massive datasets.
  • Parameters (weights) define their “knowledge.”
  • A loss function measures the gap between expected and actual output, guiding learning.

The instructor emphasizes repetition — you must revisit the material to grasp it deeply. LLMs are described as “phenomenal technology” that humanity has built over decades.

Transformers and Attention

At the core of modern AI is the transformer architecture, popularized by the 2017 paper “Attention Is All You Need.” The instructor explains:

  • Attention mechanisms let AI models “focus” on relevant parts of input.
  • Transformers use parallel processing, making them faster and more powerful.
  • GPT = Generative Pre-trained Transformer.

Example: When analyzing a paragraph, a transformer doesn’t just read word by word — it identifies which words matter most to predict meaning.

Examples and Exercises

Throughout the session, the instructor sprinkles examples to make concepts practical:

  • AI drafting agreements instead of lawyers.
  • AI assistants reducing developer costs.
  • Protein folding models predicting future diseases.
  • Tools like MidJourney making professional design accessible to anyone.

He also asks participants to rate their understanding (scale of 1–5), encouraging reflection and active engagement.

Practical Advice

Key advice from the instructor includes:

  • Don’t get lost in the hype. Learn to classify tools and concepts using the value stack.
  • Repeat recordings and exercises to strengthen fundamentals.
  • Apply AI to small projects first (summarizing text, drafting agreements, coding snippets).
  • Build a portfolio of AI-powered work to showcase skills.

Mindset for Learning AI

The instructor acknowledges overwhelm is natural — like drinking from a fire hose. But with practice, recordings, and reflection, anyone can master these concepts. The core message: AI is not about memorizing buzzwords, but about understanding how data, models, and prompts connect.

Conclusion

The AI Sprint closes with a reminder of the transformative moment we’re living through. LLMs and generative AI are compared to historical milestones in technology — breakthroughs that will shape careers and industries. By learning these fundamentals today, participants position themselves at the forefront of innovation.

Session 2: AI Product Lifecycle & Building Blocks

Discover how AI products are conceived and scaled. We’ll cover the AI Product Lifecycle, mapping real-world Gen AI use cases, and the building blocks you need to bring AI products to life. Plus, a walkthrough of Granola and ChatPRD in action.

  • The AI Product Lifecycle: from idea → prototype → deployment
  • Mapping Gen AI use cases across industries
  • Core building blocks of AI products (data, models, infra, APIs)
  • How Granola and ChatPRD demonstrate AI-driven product workflows

Slides 👇🏽

Figjam Notes

AI generated Summary of this Session: 👇
 “Please use caution with this AI generated summary. In a few cases, it may be inaccurate or misleading. Please report such instances to us through the ‘Help’ button on the bottom right of this page.”

What are you going to learn?

In this session of The AI Sprint, the instructor explains how AI products are built using transcription, summarization, and large language models (LLMs). You will learn the full workflow of tools like Whisper, how transcripts are transformed into summaries and action items, how prompts structure outputs, and how costs are calculated in terms of tokens. By the end, you’ll understand the technical stack behind products like Fireflies, Granular, and others that automate meeting notes.

Why does it matter to you?

Meetings are a daily reality in education, startups, and enterprises. Capturing what was said, condensing it into clear takeaways, and surfacing action points saves massive time. AI can do this automatically at scale. For product managers, engineers, and builders, this knowledge helps you design tools that blend APIs, prompts, and LLMs into seamless user experiences, while also considering cost and efficiency.

Key takeaways:

  1. Tools like Granular are more seamless than Fireflies because they don’t require joining the meeting manually.
  2. Whisper API is a popular transcription engine from OpenAI, converting voice to text.
  3. Raw transcripts aren’t useful; people want summaries and actionables.
  4. LLMs generate these outputs when guided with the right prompt templates.
  5. Token costs (input + output) must be estimated carefully when building AI products.
  6. Prompts, transcripts, and templates together form the AI product workflow.
  7. The cost of using LLMs scales with meeting length, transcript size, and output size.

Meeting Transcription: The Starting Point

The instructor begins by discussing common AI note-taking products like Fireflies, Phantom.ai, and Auto.ai. Their limitation is that they must “join” meetings, which can feel intrusive or unreliable if they fail to connect. By contrast, Granular activates automatically in the background, offering a smoother user experience.

Once active, the tool captures audio and uses APIs to transcribe speech into text. The leading API here is Whisper from OpenAI. Whisper not only transcribes but also supports multiple languages, making it versatile for global teams.

From Transcript to Summary

While transcription is valuable, most users don’t want to read long transcripts. Instead, they want summaries and action items. The instructor emphasizes that executives and busy professionals prefer to know “what’s next?” rather than reading full notes.

This is where LLMs come into play. By applying prompt templates, transcripts are transformed into structured outputs like:

  • Meeting title, date, and participants.
  • Key decisions.
  • Action items.
  • Open questions.
  • Topic-wise summaries.
  • Notable quotes.

These outputs are concise, clear, and actionable which is exactly what users want from AI note-taking systems.

The Role of Prompts

The instructor explains that prompts act as the instructions to the AI. A typical prompt might say:
“You are an expert chief of staff. Given the raw meeting transcript, generate a structured summary with key decisions, action items, and open questions.”

By placing this prompt on top of the transcript, the LLM knows how to organize the raw text into a usable summary. Without prompts, the model wouldn’t know what format or level of detail to provide.

Understanding Token Costs

A major focus of the session is cost estimation. Since AI APIs charge per token, builders must calculate costs based on transcript length and output size.

  • Input tokens = prompt + transcript.
  • Output tokens = summary or actionables.

For example, if a 30-minute meeting generates around 3,600–4,000 words, this becomes roughly 5,000 tokens when converted. Adding the prompt (≈250 tokens), the input total is ~5,250 tokens. If the output summary is 500–700 tokens, the total cost is based on 6,000 tokens.

The instructor also highlights that input and output tokens may be priced differently, so these must be factored in when estimating operational expenses.

The Workflow of an AI Note-taking Product

The entire process can be visualized as a pipeline:

  1. Audio capture: The meeting audio is accessed via system APIs.
  2. Transcription: Whisper API converts audio to text.
  3. Prompt template: A predefined instruction tells the model what structure to use.
  4. LLM processing: The transcript + prompt is sent to the LLM.
  5. Output generation: The LLM returns a structured summary with action items.
  6. Cost calculation: Tokens used in input and output determine billing.

This explains how products like Granular or Fireflies work under the hood, giving participants clarity on the AI tech stack powering such tools.

Real-World Examples

The instructor uses practical examples:

  • A manager doesn’t care for the raw transcript but wants the top three next steps for their team.
  • An HR leader may want decision points documented for compliance.
  • A product team may extract user quotes directly for product feedback.

These cases show why structured outputs matter more than plain transcripts.

Prompt Design and Flexibility

Prompts can be adjusted for different needs. For instance:

  • A legal team might want action items written in formal legal language.
  • A startup founder may want just bullet points of investor feedback.
  • A teacher could ask for transcripts summarized into lesson highlights.

This flexibility is what makes LLMs powerful as they can adapt summaries to different industries and user personas with just a change in prompt wording.

Conclusion

This session demystified the process of AI-driven meeting summarization. Participants learned how audio is transcribed, why raw transcripts aren’t enough, how prompts shape outputs, and how costs are calculated. The discussion of Fireflies, Whisper, Granular, and token pricing gave concrete insights into both user experience and technical implementation.

By the end, learners gained not just a technical understanding but also a product builder’s mindset: focus on user value, use prompts strategically, and always calculate costs when scaling AI tools.

Session 3: Context Engineering, RAG & Case Studies

  • What is Context Engineering and why it’s critical for AI PMs
  • Retrieval-Augmented Generation (RAG) explained with examples
  • Prompt engineering best practices for PMs
  • Fine-tuning models: when and how to apply it
  • Case Study 1: Cursor – building an AI-first developer experience
  • Case Study 2: Lovable – AI for design and productivity

Slides 👇🏽

Figjam notes

📚 Resources:

Research on “How growing context reduces the model output quality“

Guide to prompt engineering.

Cornell research paper on Prompt Engineering.

Top AI apps list by A16Z

AI generated Summary of this Session: 👇
 “Please use caution with this AI generated summary. In a few cases, it may be inaccurate or misleading. Please report such instances to us through the ‘Help’ button on the bottom right of this page.”

What are you going to learn?

In this session, the instructor dives into practical demonstrations of AI coding assistants, with a special focus on Cursor. You’ll learn how these tools understand entire codebases, use vector embeddings for context, apply large language models (LLMs) to generate features, and leverage extended context windows for handling millions of lines of code. The session unpacks the technology stack behind such tools and their real-world impact for both developers and non-developers.

Why does it matter to you?

As software projects scale, developers spend immense time reading existing code, debugging, and writing new features. AI coding assistants like Cursor can dramatically accelerate this by auto-filling code, generating end-to-end features, and understanding massive repositories. Even if you’re not technical, knowing how these systems work helps you evaluate productivity gains, understand the value of embeddings and context windows, and position yourself for careers where AI-native workflows are becoming standard.

Key takeaways:

  1. Cursor is a free tool that even non-developers can use to experience AI-driven coding.
  2. Unlike autocomplete, Cursor offers autofill for entire code blocks and features.
  3. Cursor processes the entire codebase using vector embeddings stored in specialized databases.
  4. These embeddings allow the LLM to understand coding style and context.
  5. Max Mode unlocks large context windows (200K–300K+ tokens), enabling AI to handle millions of lines of code.
  6. Tools like Cursor don’t store raw code; they store embeddings, which balances functionality and privacy.
  7. This approach combines retrieval, embeddings, and LLM generation — the heart of modern AI-assisted development.

Introduction to Cursor

The instructor begins by encouraging everyone — even non-technical participants — to install Cursor, a free AI-powered code editor. Cursor works in two main ways: it autofills code as you write and it uses full codebase understanding to generate end-to-end features.

Unlike traditional autocomplete, which predicts only the next character or line, Cursor can fill entire functions or suggest full implementations based on context. For example, when a developer writes a partial function header, Cursor can generate the entire body with relevant logic, saving significant time.

How Cursor Understands the Codebase?

Cursor doesn’t just rely on isolated prompts. It processes the entire codebase by breaking it into chunks, converting these chunks into vector embeddings, and storing them in a database (referred to in the session as “Karzer’s database”).

When you ask Cursor to implement a feature, it retrieves relevant embeddings to understand how the project is structured — coding style, architecture, variable naming, and prior implementations. Then, it leverages an LLM (like GPT or Claude) to generate the new code.

This retrieval-augmented generation ensures the AI not only “writes code” but writes it in your project’s language. For instance, if your project consistently uses snake_case for variables, Cursor’s suggestions will match that style.

The Role of Vector Embeddings

The instructor highlights that Cursor never directly stores your raw codebase. Instead, it stores embeddings numerical representations of the code’s meaning. This has two benefits:

  • Privacy: Sensitive source code isn’t stored directly.
  • Efficiency: Embeddings make it easy to retrieve context quickly for LLM queries.

For example, if a project has a million lines of code, embeddings allow the assistant to find just the relevant few hundred lines needed to complete your request.

End-to-End Feature Generation

Cursor is capable of handling complex feature requests. If you ask it to “write a login feature with password reset,” it will:

  1. Retrieve relevant context from embeddings (e.g., how authentication is currently handled).
  2. Use an LLM to draft the full feature, respecting your coding patterns.
  3. Present the generated code as an autofill suggestion for integration.

This drastically reduces the manual effort of developers and allows even small teams to ship faster.

The Power of Max Mode

A standout capability of Cursor is Max Mode, which expands the usable context window. Normally, models can handle 200K–300K tokens of context. With Max Mode, Cursor (through partnerships with GPT and other providers) allows far larger context windows.

This means that instead of just understanding a few files at a time, Cursor can ingest entire repositories with millions of lines of code. For enterprise-scale projects, this is a game-changer — the assistant can “see” the whole system before generating suggestions.

Example in Practice

Consider a team working on a massive e-commerce platform. Traditionally, onboarding a new developer might take weeks because they need to study the existing architecture. With Cursor:

  • The embeddings already capture the project’s design.
  • A developer can request: “Write a new API endpoint for order tracking.”
  • Cursor uses embeddings to understand existing APIs, retrieves relevant context, and autofills a new endpoint consistent with the project’s style.

This doesn’t eliminate the need for human review, but it significantly accelerates development.

Codebase Privacy and Embeddings

A recurring concern in AI coding is security. The instructor reassures participants that Cursor does not upload raw source code to its servers. Instead, only embeddings — mathematical abstractions — are stored. This means the tool can provide contextual assistance without exposing sensitive intellectual property.

This design choice balances developer trust with AI utility, making Cursor suitable even for enterprise contexts where data privacy is paramount.

Broader Implications

The session illustrates how tools like Cursor represent a paradigm shift in software development:

  • They integrate retrieval systems (to find context).
  • They apply embeddings (to encode knowledge).
  • They leverage LLMs (to generate code).

Together, this triad forms the backbone of modern AI-assisted development. For non-technical professionals, the takeaway is that AI can now handle complex creative tasks — not just predict text, but build structured, functional software.

Conclusion

By the end of the session, the instructor makes it clear that tools like Cursor aren’t just productivity hacks. They embody the next stage of AI’s evolution: blending retrieval, embeddings, and generation to work across vast contexts. For developers, this means faster coding, fewer errors, and a powerful assistant that understands entire codebases. For non-developers, it signals how AI is reshaping industries by lowering technical barriers.

The key lesson is that mastering these tools or at least understanding how they function — is crucial for anyone building a career in today’s AI-driven landscape.


 Resources:

Research on “How growing context reduces the model output quality“

Guide to prompt engineering.

Cornell research paper on Prompt Engineering.

Top AI apps list by A16Z

Session 4: AI Agents, Vibe Coding & Career Prep

  • Understanding AI Agents: autonomous decision-making systems
  • Vibe Coding: building human-friendly AI experiences
  • Creating your AI Product Portfolio with real projects
  • Interview prep for AI Product Manager roles: skills, frameworks, and questions
  • Pathways to becoming an AI Product Manager in top tech companies

Slides 👇🏽

FigJam Notes.

📚 Resources:

Lovable tutorial from Lovable.

Summer of Product Playlist. 

AI generated Summary of this Session: 👇
 “Please use caution with this AI generated summary. In a few cases, it may be inaccurate or misleading. Please report such instances to us through the ‘Help’ button on the bottom right of this page.”

What are you going to learn?

In this session, the instructor explains how AI products like Granular transform raw meeting audio into actionable insights. You’ll learn the entire workflow — from capturing audio, using transcription engines like Whisper, structuring prompts, sending data to large language models (LLMs), and estimating token costs. The session also explores feasibility, usability, and real-world applications of AI note-taking tools.

Why does it matter to you?

Meetings are central to work life, but raw transcripts are overwhelming and time-consuming. AI-driven systems save hours by automatically producing summaries, decisions, and action points. For anyone building AI products, understanding the tech stack, cost model, and user value is critical. For professionals, it highlights how these tools enhance productivity and decision-making.

Key takeaways:

  1. Granular offers seamless meeting capture compared to Fireflies or other tools that require joining calls.
  2. Whisper API is widely used for transcription into text and multiple languages.
  3. Raw transcripts have little standalone value; summaries and actionables are what users need.
  4. Prompts act as templates to guide LLMs in structuring useful outputs.
  5. Costs depend on input tokens (transcript + prompt) and output tokens (summary/action items).
  6. The AI product workflow blends APIs, prompts, LLMs, and token management.
  7. Product success depends on three checks: desirability (valuable), feasibility (technically possible), and usability (solves real problems).

From Listening to Transcript

The instructor begins by explaining how note-taking products listen to meetings using system APIs on Mac or Windows. After gaining microphone permission, the audio is captured and passed to transcription services. The most popular tool is OpenAI’s Whisper API, which converts speech into text and even supports multi-language transcription.

While this produces transcripts, the instructor emphasizes that no one reads transcripts. Busy professionals don’t want word-for-word notes — they want what was decided and who needs to do what.

From Transcript to Summary

The next step is transforming transcripts into structured outputs. This is done using large language models, but the key is prompt engineering. A carefully designed prompt might ask the LLM to create:

  • Meeting title, participants, and date.
  • Key decisions.
  • Action items.
  • Open questions.
  • Topic-wise summaries.
  • Notable quotes.

For example, a legal team may want a formal output with contract terms, while a product manager may just want bullet-pointed feedback. The same transcript can yield different summaries depending on the prompt template.

Estimating Token Costs

A major part of the session is about cost calculation. LLMs charge per token, and every meeting transcript translates into thousands of tokens. The instructor walks through an example:

  • A 30-minute meeting = about 3,600–4,000 words.
  • Words are converted to ≈5,000 tokens.
  • Add a prompt (≈250 tokens).
  • Total input = ~5,250 tokens.
  • A summary output = ≈500–700 tokens.

Thus, each call might consume ~6,000 tokens, and costs vary depending on the pricing for input vs output tokens. Builders must design products keeping these economics in mind.

The Workflow of AI Products

The session outlines the AI product pipeline:

  1. Audio capture via APIs.
  2. Transcription with Whisper.
  3. Prompt template added on top of transcript.
  4. LLM processing generates summaries and actionables.
  5. Outputs delivered to the user.
  6. Token cost estimation ensures feasibility.

This systematic process makes AI tools like Granular valuable and practical.

Product Value and Feasibility

The instructor uses a desirability-feasibility-usability framework to evaluate these products.

  • Desirability: Professionals want summaries and actionables, not raw transcripts. This solves a real pain point.
  • Feasibility: APIs like Whisper, calendar APIs, and LLMs make it technically possible. The instructor stresses that feasibility is proven — these systems already work.
  • Usability: Users save time by avoiding manual note-taking or memory lapses. For example, instead of remembering who promised what, a system can email all decisions automatically.

Practical Applications

The instructor gives relatable scenarios:

  • Executives want three clear next steps after a meeting.
  • Teachers could use it to summarize student discussions.
  • Startups can automate investor call notes.
  • Legal teams may format outputs into compliance-friendly documents.

These show how prompts allow personalization for each industry.

Key Insights

  1. Granular’s edge: Unlike Fireflies or similar apps, Granular doesn’t need to “join” meetings manually it runs in the background, providing a smoother UX.
  2. Prompts = Product logic: Changing the prompt can completely change the product’s value for different users.
  3. Costs are critical: Without understanding tokens, AI products risk being unscalable.
  4. LLMs are flexible but expensive: Builders must balance usefulness against cost.

Conclusion

The instructor wraps up by reinforcing that AI products succeed when they deliver actionables, not just information. For meeting productivity tools, the winning formula is: capture → transcribe → summarize → action. The technical stack is feasible today with Whisper, LLMs, and prompt templates, but the real challenge is balancing usability and cost.

By the end of the session, participants understood not only the technology pipeline but also the business considerations of building AI-powered productivity tools.