The Product Compass

Helped thousands of professionals boost their product management skills by sharing knowledge and experience in online courses, newsletter, and social media posts.

Author, Founder

Ideals

Ideals helps professionals to collaborate over sensitive data and run critical business transactions, such as M&A, smoothly. Trusted by 1M+ leading professionals globally.

Managed the core product team responsible for documents, document workflows, and permissions.

Increased CSAT from 52% to 86% and CES from 78% to 85% by pioneering a new approach to product analytics and collaborating with designers and engineers.

Enabled upmarket deals and international expansion by improving performance by over 100% and eliminating scaling limitations.

Identified 2 critical revenue growth opportunities and launched the first through market research and collaboration with Sales, CSM, Product, Design, and Engineering.

Senior Product Manager

Regiondo 

Regiondo is Europe's leading booking software for the leisure industry. More than 7,500 tour and activity providers from 42 countries power their revenue using its booking software (SaaS, B2B2C).

Product Manager

BEC Poland

Launched a product integrating dozens of systems and 30+ banks in Denmark by collaborating with 5 
cross-functional teams, managing 50+ stakeholders and suppliers and reporting directly to the BEC Directors.

Enabled BEC to shut down legacy systems by prioritizing product roadmap and timely shipping a platform that covers virtually all banking areas. In June 2022, the platform was handling over 2.3M active end-users.

Led the implementation of a complex B2B financial system that saved 10M+ EUR by discovering and prioritizing ideas, setting and tracking the key product metrics, and planning A/B experiments.

Epic Owner

APN Promise S.A.

Transformed how the company operates in 5 product areas: vision and strategy, customer value, driving business results, marketing, quality, and team building, as confirmed by Tomasz Sochacki (former IT Director) on a LinkedIn profile.

Launched and got early adopters for a new B2B product by conducting market research, analyzing customer pain points, testing ideas, prioritizing the roadmap, and leading a Scrum team.

HURO Sp. z o.o.

Built and scaled a tech product company offering reusable document workflows and a plug-and-play Intranet and reached over 100,000 MAU (B2B, SaaS and on-premise), even though HURO started with virtually no capital.

Won over 15 B2B enterprise contracts, including LOT Polish Airlines, PSE and LOTOS (Oil and Energy), OTIS (US, elevators), Pratt & Whitney (US, production), Polish National Court, Polish Air Navigation Agency, GKN Driveline (FTSE 100), PKO Bank and Nest Bank, and more, by setting a clear vision and strategy, managing sales and marketing, setting objectives, running hundreds of experiments, and leading 2 cross-functional teams.

Executed the exit strategy by selling all shares (50% of the company) in March 2019.

Founder, Chief Product Officer

ITMAGINATION

Built a new software department (Enterprise Collaboration) by taking ownership of sales, marketing, hiring, coaching, leading teams and managers, and financial results.

Program Manager

Launched and coordinated the go-to-market of a new financial platform on the market (B2C) by saving a challenging project, after stepping in as a Team Leader in the middle of the development. As a result, promoted to a higher position.

Team Leader and Architect

SII Polska

Lead .NET Developer

Altkom Akademia S.A.

.NET Developer

ZieluONE

AI & Product Management | Founder, Author @ The Product Compass Newsletter | Join 119,000+ PMs

Follow me for actionable tips, resources, and step-by-step playbooks for AI and AI-empowered PMs. 

The Product Compass Newsletter trusted by 119,000+ PMs: https://www.productcompass.pm/

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I can no longer accept connections because LinkedIn limits the number of 1st-level connections to 30K. 

To connect with me, join our premium Slack community: https://www.productcompass.pm/p/product-management-resources, or engage with my content.

Paweł Huryn

Senior AI PM makes $184K (US) with Meta with paying $737K+.
I've analyzed over 100+ AI PMs job offers. 
5 most common skills they're looking for:

1. AI Product Lifecycle (82%)

Example: Owning product strategy, vision, and go-to-market (AWS).

Start here:
→ How to Create an AI Product Strategy: https://lnkd.in/ds9EQ7hS
→ How to Design a Value Proposition: https://lnkd.in/du9zcZDA
→ 5 GTM Principles You Should Know as a PM: https://lnkd.in/dEPMtmKU
→ AI Evals for PMs: https://lnkd.in/dHUFrkVs
→ How to Evaluate AI Agents: https://lnkd.in/dKM9YvNW

2. Cross-Functional Collaboration (76%)

Example: Collaborate with cross-functional teams, including engineering, UX/UI, and business stakeholders (Google).

Start here:
→ WTF is an AI Product Manager: https://lnkd.in/dBZUmDHq
→ What is Product Discovery: https://lnkd.in/dNUB__n3
→ AI Prototyping: https://lnkd.in/dPaKYv7S
→ Experiments Library: https://lnkd.in/dvUisjR2
→ AI PRD Template: https://lnkd.in/dQ2jiMfr
→ Product Discovery Course (free for premium subscribers): https://lnkd.in/eianWDnk

3. Gen AI, LLMs, and AI Frameworks (69%)

Example: Experience with Generative AI or LLMs (T-Mobile).

Start here:
→ Neural Networks, Transformers, and LLMs: https://lnkd.in/dveWsD9f
→ AI PM Glossary: https://lnkd.in/dVCki_gf
→ 14 Prompting Techniques for PMs: https://lnkd.in/dnCGVH6a
→ Fine-tuning for PMs: https://lnkd.in/dwJfQiC7
→ Context Engineering for PMs: https://lnkd.in/d_XVN5KV
→ AI Agent Architectures: https://lnkd.in/dnmQ_kP8
→ The Ultimate Guide to n8n for PMs: https://lnkd.in/dKYZ5Y3n

4. Data-Analytics for AI (67%)

Example: Define and track key performance indicators (KPIs) for product success (ZnA).

Start here:
→ How to Find The Right AI Product Metrics: https://lnkd.in/d9tZcvVT
→ Cohort Analysis: https://lnkd.in/daJ5KQWW
→ Funnel Analysis: https://lnkd.in/d8Gt5ECV
→ AARRR Metrics: https://lnkd.in/dpM_9A55
→ North Star Metric: https://lnkd.in/dfBNB77S

5. AI-Driven Customer Experience (48%)

Example: Create conversational experiences that are personalized, relevant, and grounded in trusted information (ServiceNow).

Evals are not enough. Develop an AI intuition by building, not theorizing. Step-by-step guides (no coding):
→ RAG chatbot: https://lnkd.in/dew--RqD
→ AI voice agent: https://lnkd.in/dSW6_KiT
→ Multi-agent research system: https://lnkd.in/dpBGF3pq

More in the comments.
What did I forget?

Author & Founder | First-Hand Insights and Step-by-Step Playbooks for AI Product Managers

I got permission to publish a new AI Evals FAQ. It’s basically a bible for engineers and AI PMs 🤯

Dario Amodei (Anthropic CEO) said, “The scarcest skill in AI right now isn’t prompt engineering… it’s product managers who actually understand evaluation.”

The authors of the guide are Hamel Husain and Shreya Shankar, who run the world’s No. 1 AI Evals course by any metric.

Together with top AI architects and ML researchers, they answer the most common questions from 3,000+ students.

It's massive. And 100% free.

Some of the questions answered:

Q: What are LLM Evals?
Q: What’s a minimum viable evaluation setup?
Q: Why is "error analysis" so important in LLM evals, and how is it performed?
Q: What is the best approach for generating synthetic data?
Q: Are there scenarios where synthetic data may not be reliable?
Q: Why do you recommend binary (pass/fail) evaluations?
Q: Should I use "ready-to-use" evaluation metrics?
Q: How many people should annotate my LLM outputs?
Q: Should PMs and engineers collaborate on error analysis? How? 
Q: What parts of evals can be automated with LLMs?
Q: Should I stop writing prompts manually in favor of automated tools?
Q: What makes a good custom interface for reviewing LLM outputs?
Q: What gaps in eval tooling should I be prepared to fill myself?
Q: How should I version and manage prompts?
Q: How are evaluations used differently in CI/CD vs. monitoring production?
Q: What’s the difference between guardrails & evaluators?
Q: Is RAG dead?
Q: How should I approach evaluating my RAG system?
Q: How do I evaluate sessions with human handoffs? 
Q: How do I evaluate complex multi-step workflows? 
Q: How do I evaluate agentic workflows? 

Hope that helps!
Feel free to share it with your network.

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P.S. Want to test your knowledge on AI Evals?

🎁 Hamel created a great free quiz on AI Evals. You get 20 random single-choice questions each time.

After submitting, you’ll see:
→ Your points and correct answers
→ A detailed explanation for each option
→ Free learning materials to help you study and improve

You can take the test here: https://lnkd.in/daTu-HBf
(no marketing consent required)

I also recommend the AI Evals for Engineers and PMs course. I attended the first cohort: https://bit.ly/aievals35

AI Evals Bible for Engineers and PMs, Nov 2025

I built a RAG chatbot in 45 minutes. No coding. It's a great way to learn or create a solution for an AI PM portfolio.

But how does it work?

First, RAG isn't just about vector stores. It might involve retrieval from any data source like Google Drive, SQL, or text files.

I used a vector store, so I needed to:

𝐒𝐭𝐞𝐩 𝟏: 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬

The data isn’t stored in its original format.

Instead, it's split into chunks (e.g., 500-1000 characters each), which are then converted into multi-dimensional vectors and stored in a vector database.

𝐒𝐭𝐞𝐩 𝟐: 𝐇𝐚𝐧𝐝𝐥𝐞 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥, 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐔𝐈

The users interact with a Lovable interface. When someone asks a question, it's turned into a vector and used to retrieve the most relevant chunks.

Finally, an LLM uses retrieved chunks and the original request to generate an answer.

That's Vanilla RAG - the simplest possible implementation.

In practice, consider:

• 𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐑𝐀𝐆: Dynamically selects or modifies the retrieval strategy (e.g., choosing the best data source, reformulating the query) based on the request.
• 𝐇𝐲𝐛𝐫𝐢𝐝 𝐑𝐀𝐆: Combines multiple retrieval approaches, e.g., keyword + semantic search, or merges results from multiple sources.

𝐒𝐭𝐞𝐩 𝟑: 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐑𝐀𝐆

RAG systems have two distinct components that require different evaluation approaches: retrieval and generation. 

Traditionally, we evaluate the retrieval part using metrics like Recall@k, Precision@k, or MRR.

I loved Jason Liu’s post "There Are Only 6 RAG Evals." He noticed that RAG systems have 3 core components:
• A question (Q)
• Retrieved context (C)
• An answer (A)

In his post, he explains how to evaluate each of those combinations: https://lnkd.in/dGgcqKB5

You might learn more from: https://lnkd.in/d9tZcvVT

𝐓𝐡𝐞 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐞𝐝 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤

You can build it virtually for free:
• UI: Lovable (free version is enough)
• Orchestration: n8n (trial / free self-hosted edition)
• LLM: GPT-5-mini by OpenAI (less than $2 for 100s of requests)
• Embedding model: text-embedding-3-small
• Vector database: Pinecone (free tier, Starter)
• Documents' source: Google Drive

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In my post, I explain how to build a RAG chatbot step-by-step. No coding. 

This might help you:
• Learn by doing
• Develop a better AI intuition
• Build an AI chatbot for your portfolio

Demo, templates, and more with a free trial: https://lnkd.in/dew--RqD

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P.S. Want to break into AI PM?

I highly recommend the AI PM Certification. It’s a 6-week, hands-on cohort led by Miqdad Jaffer (Product Lead at OpenAI).

I joined the Spring 2024 cohort and loved the mix of practical projects and networking.

The next session starts Jan 26. I secured a $500 discount for our community: https://bit.ly/aipmcohort

#1 AI & Product | Actionable Insights & Step-by-Step Playbooks for AI PMs | Join 127,000+

Many PMs struggle to explain the difference between Vision, Strategy, Objectives, and Roadmap. But those are extremely simple concepts.

Let's tackle them one by one:


1. Product Vision (Why)

Product Vision is the long-term aspiration of your product. It motivates your team to wake up every morning and go to work.

For example, “Send humans to the Moon” or “Help tour operators focus on doing what they love.”

Effective vision needs to be inspiring and emotional. It becomes much more memorable when it speaks to people's hearts.

My favorite example is the JFK Moon speech: https://lnkd.in/dSEUENZk 


2. Product Strategy (Where & How)

Strategy is not a plan, a goal, or a set of actions.

It's a cohesive set of choices that you believe will allow you to win (achieve your Vision) at the playing field of your choice:

- market and its constraints (e.g., geography)
- value proposition
- relative costs
- tradeoffs
- growth

The strategy should pass the “can’t / won’t test” so that competitors can't copy it. Some refer to it as "moat" or "unfair advantage."

A fantastic, short video by Prof. Roger Martin: https://lnkd.in/d6Dbnmg4 

The Product Strategy Canvas (no email, no paywall): https://lnkd.in/d3w3QUwV 


3. Objectives (What)

In the product model (read TRANSFORMED), empowered teams get meaningful problems to solve and clear desired outcomes.

As we "lead with context, not control," objectives must be embedded in the strategic context.

I encourage you to use the OKR framework. It's a simple, incredibly effective approach for setting, monitoring, and achieving your goals. 

While following the SMART goals principle (Specific, Measurable, Achievable, Relevant, and Time-Bound), OKRs add an additional, inspirational aspect to the Objective.

Best book: Radical Focus by Christina Wodtke


4. Product Roadmap (So What & When)

Product Roadmap is a communication tool that allows you to align everyone in the organization. It creates focus on what’s important right now. It should also explain the reasoning behind it.

It's essential to:

- Focus on outcomes, not features
- Do not commit too soon
- Shorten the planning horizon

My favorite format is the Now-Next-Later format by Janna Bastow.


More information: 

- Vision, Mission, and Purpose are often confused. I use a single term. An article by Prof. Roger Martin: https://lnkd.in/d9tXqe77 

- According to Prof. Martin, Vision is part of Strategy. I agree. Strategy is an integrated set of choices. And you develop them together, not in a sequence. But many leaders don't get that. So, during interviews, keep it simple.


Hope that helps. 

What are your thoughts?

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P.S. Like this infographic?

You can download 50+ PM learning PDFs here: https://lnkd.in/dkBsZ-ZY 

P.P.S. Interested in AI Product Strategy?

See 2 posts by OpenAI leader:
- AI Strategy and Leading AI Initiatives: https://lnkd.in/dShxwfsi
- 3-Layer AI Product Distribution Framework: https://lnkd.in/dejzkwYG

Product Vision vs. Strategy vs. Objectives

Lovable is my favorite no-code tool for PMs. But there’s always been one blocker: You need a $50–$200/month to build something useful. 

So I kept searching for something I could confidently recommend to my readers.

I finally found it.

Dyad — a free, local, open-source app builder.

I was surprised. It lets you prototype, learn, and ship locally for $0.00. 

For many cases, that’s all you need to start developing AI intuition and confidence.

I genuinely loved using it. 

In one evening it built a Trello Clone PoC:

- Boards, lists, and cards with drag & drop
- AI generating lists and cards
- AI suggesting background images
- Using labels and marking cards as complete
- Authentication and permissions

All with a free stack ($0.00) and free Gemini API keys ($0.00).

The free Dyad tier has everything you need to start. It syncs with GitHub, connects to Supabase, lets you publish your apps on Vercel/Netlify and switch LLMs anytime.

No vendor lock-in.

Dyad is a great alternative for anyone who wants to prototype, learn, or build simple products.

From my free post you will learn:

1. Example: What Apps You Can Build With Dyad
2. How to Prototype and Build Apps for Free
3. Example: Adding a “Mark Boards as Favorite” Feature to My Trello Clone in Dyad
4. Dyad vs. Lovable: When to Use Which
5. Best Practices When Working With Dyad
6. Bonus: Dyad vs. Lovable vs. Base44 vs. Bubble vs. Bolt vs. Google Firebase Studio vs. Replit vs. Google Jules vs. OpenAI Codex vs. Visual Studio Code vs. Cursor 😀

🎁 No email, no paywall, no stupid comments: https://lnkd.in/dXYyyEBP

Hope that helps!

(This post isn’t sponsored.)

It takes 4-6 years for an AI PM to get to $500k comp.
It takes 8-12 years for most PMs to get to $300k comp.
Here's how to become an AI PM:

But what is an AI PM?

I love simple definitions:
An AI PM is a PM who works on AI-powered products or features.

So, in this post (packed with resources for each level):

1. What is a Product Manager?
2. What is an AI-Powered PM?
3. What’s Special About AI PM?

Let's dive in.

1. What is a Product Manager?

As Marty Cagan explains in Inspired: „The first truth is that at least half of your ideas are just not going to work.”

The most important activity for a PM is participating in product discovery. The goal is to figure out what to build and address key risks before implementation.

As a PM, you’re responsible for value and viability risks more than anything else.

Before diving into AI, you should get discovery right. Start here:

• What is Product Discovery: https://bit.ly/pdisc101
• How to Manage Risks as a PM: https://bit.ly/43rVRvp
• The Ultimate Experiments Library: https://lnkd.in/djpPCm4c

2. What is an AI-Powered PM?

An AI-Powered PM is just a PM who uses AI at their work.

My strong opinion: Soon, there will be no such thing as a PM who doesn’t use AI virtually daily. We should be living and breathing AI.

Not to replace our thinking. But to boost our productivity and eliminate manual work. Here's what you need to know:

• 14 Prompting Techniques for PMs: https://bit.ly/3LEQfIg
• The Ultimate Prompt Library: https://bit.ly/4oLusgw
• How to Automate Figma → Jira: https://bit.ly/43ti10s
• AI Prototyping for PMs: https://bit.ly/43rDrev

3. What’s Special About AI PM?

The role is more technical. You don't need to code. But AI PMs must understand the technology and develop an AI intuition. The plan:

• Neural Networks, Transformers and LLMs: https://bit.ly/4lHoVFY
• Fine-Tuning Step-by-Step: https://bit.ly/4de9jGE
• Context Engineering: https://bit.ly/47XX3b8
• AI Agents & Architectures: https://bit.ly/3H6I2de
• RAG Step-by-Step: https://bit.ly/3RLUHER
• How to Build a Voice Chatbot: https://bit.ly/453UdS7
• The Ultimate Guide to n8n: https://bit.ly/3XuwW6U
• AI Strategy: https://bit.ly/47EfL8Y

One new element is AI evals. Unlike unit tests, they are closely related to the PM's risk areas (value, viability). 

You will collaborate to review traces, identify failure modes, and experiment with prompts. Here’s everything you need to know:

• Mastering Evals for PMs: https://lnkd.in/dHUFrkVs
• Error Analysis for PMs: https://bit.ly/3M0Hmso

Hope that helps!

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P.S.: If you want to become an AI PM, Product Faculty's AI PM Certification with OpenAI’s Product Lead is the best path (don’t just take my word, that’s what 600+ alumni say):

• 3,000+ PMs trained
• 600+ reviews (highest on Maven)
• The next cohort starts on Jan 26
• $500 off + $10K worth of resources included

Go here: https://bit.ly/aipmcohort

Picture by Aakash Gupta

The Ultimate MBA for AI Product Managers:
(better than any $124K program):

Basic Concepts:
- Introduction to AI PM: https://lnkd.in/dveWsD9f
- AI PM Glossary: https://lnkd.in/dVCki_gf
- WTF is an AI PM: https://lnkd.in/dBZUmDHq

Prompt Engineering:
- 14 Prompting Techniques Every PM Should Know: https://lnkd.in/dnCGVH6a
- Top 9 High-ROI ChatGPT Use Cases for PMs: https://lnkd.in/dsp_-RYA
- The Ultimate ChatGPT Prompts Library for PMs: https://lnkd.in/dQV66YJt

Fine Tuning:
- A Practical Guide to Fine-Tuning for PMs: https://lnkd.in/drmiwKuq

RAG:
- A Guide to Context Engineering for PMs: https://lnkd.in/d_XVN5KV
- How to Build a RAG Chatbot Without Coding: https://lnkd.in/dew--RqD
- An interactive RAG simulator: https://lnkd.in/dsi6C5b9

AI Agents:
- The Ultimate Guide to AI Agents for PMs: https://lnkd.in/dkUrHKP2
- AI Agent Architectures: The Ultimate Guide With n8n Examples: https://lnkd.in/dnmQ_kP8
- MCP for PMs: How To Automate Figma → Jira (Epics, Stories) in 10 Minutes: https://lnkd.in/dx2kesVT
- J.A.R.V.I.S. for PMs: Automate Anything with n8n and Any MCP Server: https://lnkd.in/dSW6_KiT
- I Copied the Multi-Agent Research System by Anthropic.: https://lnkd.in/dpBGF3pq

AI Prototyping:
- AI Prototyping: The Ultimate Guide For PMs: https://lnkd.in/dPaKYv7S
- A Complete Course: How to Build a Full-Stack App with Lovable (No-Coding): https://lnkd.in/dWU6yTK7

AI Evals:
- AI Evals - A Massive FAQ (PDF): https://lnkd.in/dvw-yBQ9
- Mastering AI Evals (Introduction): https://lnkd.in/dHUFrkVs
- AI Evals: How to Find The Right AI Product Metrics (Error Analysis): https://lnkd.in/d9tZcvVT
- The Ultimate Guide to AI Observability and Evaluation Platforms: https://lnkd.in/ddQTFw6n

Building SaaS with AI:
- How to Build and Scale Full-Stack Apps in Lovable Without Breaking Production: https://lnkd.in/duepvFEB
- Lovable Just Killed Two Apps? Create Your Own SaaS Without Coding in 2 Days: https://lnkd.in/eYMhQAvN
- 17 Penetration & Performance Testing Prompts: https://lnkd.in/dvZaUJ-i
- The Rise of Vibe Engineering: Free Courses, Guides, and Resources: https://lnkd.in/d_NhTPM9

What would you add the the list?

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P.S. Want to break into AI PM? I recommend the AI PM Certification by Miqdad Jaffer (Product Lead at OpenAI). I lead the AI Build Labs. We start on Jan 26, 2026. $500 off: https://bit.ly/aipmcohort

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🎁 An online version of the Ultimate AI PM Learning Roadmap with dozens of free guides and resources: https://lnkd.in/dkmU7_gr

All AI PM Articles (strategy, monetization, vibe engineering): https://lnkd.in/dTv4r9Pi

Google just dropped the Gemini File Search API (RAG-as-a-Service). It allowed me to build a RAG chatbot in 31 min 🤯

Here’s how it works:

You upload your files and immediately get:

 • Semantic search over your content.
 • Grounded answers with citations.
 • Support for common text file types.

Just one tool.
Without multi-week discussions or building expensive infrastructure.

This is perfect for:

 • Prototyping and testing ideas fast.
 • Building agents that need fast access to your docs.

To avoid hype, there are also real tradeoffs:

 • You cannot pick or tune embedding models.
 • You cannot change how retrieval is ranked.
 • You cannot inspect embeddings or scores.

To avoid hype, there are also real tradeoffs:

---

How to start?

When I tested it with coding agents (Lovable, Claude Code), they struggled to understand Google’s examples (a common issue with new APIs similar to the old ones).

So, I created a detailed 𝗳𝗿𝗲𝗲 𝗵𝗮𝗻𝗱𝗯𝗼𝗼𝗸 to help you make it work reliably in just a few prompts: 𝗚𝗲𝗺𝗶𝗻𝗶 𝗙𝗶𝗹𝗲 𝗦𝗲𝗮𝗿𝗰𝗵 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗛𝗮𝗻𝗱𝗯𝗼𝗼𝗸.𝗺𝗱 

Inside:

 • What Gemini provides,
 • What you must build,
 • Key architecture decisions,
 • Tested code snippets (metadata, stores, search),
 • Implementation patterns,
 • Best practices,
 • Common mistakes,
 • Limitations.

You can read it or drop into your agent and let it guide the implementation.

🎁 It’s free to download from my post (no email, no paywall, no comments required): https://lnkd.in/duRJ7hDj

With my free analysis:

 • Why PMs Should Consider Gemini File Search
 • Example: Little DMS (Document Management System)
 • Gemini File Search vs Traditional RAG
 • How to Easily Implement Gemini File Search in Your Product

Hope that helps. 
Prototyping RAG has never been this easy!

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Bonus: Premium subscribers can also get a working Little DMS template shown in the video below. This is optional and doesn’t block access to the handbook.

User Stories are narratives that foster collaboration, guide development and drive meaningful outcomes.

But many use them as containers for requirements.

So here's User Stories 101:

𝟭. 𝗪𝗵𝘆?

A User Story is written from the perspective of a user who wants to derive value from the product. 

It should focus on the user's desired outcomes and be embedded in the context where the user seeks value.

𝟮. 𝗛𝗼𝘄?

The basic format of a User Story is simple:

WHO: As a [user type]
WHAT: I want [action to perform]
WHY: So that [the desired outcome]

A prerequisite to working with User Stories is understanding the users, ideally segmented by their needs and desired outcomes.

𝟯. 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗨𝘀𝗲𝗿 𝗦𝘁𝗼𝗿𝗶𝗲𝘀: 𝟯 𝗖'𝘀

The 3 C's of User Stories are essential for writing a good User Story:

- Card

A card explaining WHO, WHAT, and WHY. They once were physical cards, but today, we create User Stories in tools like Jira.

- Conversation

Rather than creating a detailed specification, discuss the reasoning behind the User Story. 

Everyone needs to understand not just how it will benefit the users but also how it will contribute to the Product Outcome and how it aligns with your product strategy.

- Confirmation

Confirmation refers to the Acceptance Criteria that should be fulfilled to ensure the requirements have been met and delivered correctly. It's a concise checklist, not detailed test cases. 

𝟰. 𝗧𝗵𝗲 𝗜𝗡𝗩𝗘𝗦𝗧 𝗨𝘀𝗲𝗿 𝗦𝘁𝗼𝗿𝗶𝗲𝘀

INVEST is an acronym representing the essential qualities of well-written User Stories:

- Independent: Each User Story should be self-contained and not depend on other stories.

- Negotiable: The details of the User Story can be discussed and negotiated with the team and the stakeholders.

- Valuable: The User Story delivers value to the user.

- Estimable: It should be possible to estimate the effort required to complete the User Story.

- Small: User Stories should be small enough to be completed within a single iteration. If you do not work in iterations, you might assume 2-4 weeks as the rule of thumb.

- Testable: There are clear criteria to determine if the story has been successfully implemented.

Those qualities are desirable. But don't obsess over the acronym.

𝟱. 𝗛𝗼𝘄 𝘁𝗼 𝗦𝗽𝗹𝗶𝘁 𝗨𝘀𝗲𝗿 𝗦𝘁𝗼𝗿𝗶𝗲𝘀

Some User Stories are too big to be selected for the implementation. Break them into smaller, more manageable pieces to deliver value faster and, ideally, start learning from customers using your product for real.

- Epic: A larger element, typically combining 5-15 smaller User Stories (a rule of thumb).

- User Stories: Small, manageable items.

- Tasks: Used to plan and monitor the exact work performed by Engineers. PMs have nothing to say here.


Some add more layers, but in my experience, you don't need that.

Keep it simple.

Hope that helps!

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P.S. You can download 50+ PM learning infographics as PDFs (all the most critical concepts) here: https://bit.ly/pmposters

Amazon Web Services (AWS)

Senior Cloud Infrastructure Architect

Cloud Infrastructure Architect

Synamedia

Senior Software Engineer

Cisco

Vision3 Solutions Inc

Lead Software Engineer

Sloka Inc

Software Engineer

AWS Certified Solution Architect Associate with 10 years of experience in the IT industry comprising of optimizing mission-critical deployments in AWS, Python scripting, Kubernetes Containers Orchestration, Terraform IaC, CI/CD, test and deployment automation, and many DevOps processes. Exceptional at analysis and evaluation of an issue in order to form a judgment. Earned Master’s degrees in both computer science and business administration.  Currently working in the USA on H1B work visa and need to get H1B transfer done before I start working for a new employer.

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