I build AI products from zero to one, and I teach others how to do the same.Over 12+ years, I've taken AI/ML products from proof-of-concept to launch at Amazon, Nike, T-Mobile, and Disney. The work I love most is early-stage: validating ideas, getting executive greenlight, and shipping MVPs that prove value before scaling. At Nike, that approach drove a $100M annual reduction in product returns.I also teach what I build. I co-created and lead the AI Product Management Certificate at the University of Washington, where experienced PMs ship real GenAI products complete with evaluation plans and GTM strategies. Previously, I trained 4 cohorts at Product School.What I bring: the ability to translate messy AI capabilities into clear product value, align technical and business stakeholders, and move fast without breaking trust. I write about AI PM craft at aipmguru.substack.com.I'm currently exploring opportunities to lead 0→1 AI product work at companies solving meaningful problems. I'm especially energized by AI in healthcare and education, though I'm open to any domain where the product can genuinely improve lives. 

Shaili Guru

This message made my morning ☀️

A reader just shared that they landed an AI Product Manager role, and mentioned my content helped along the way.

Here's the thing though: I'm glad the content helped, but they put in the work.

The learning. The interviewing. The career pivot. That's all them.

Moments like this remind me why I write. Not to have all the answers, but to make the path a little clearer for folks navigating the transition.

If you're in the middle of your own AI PM journey right now, keep going. It's working.

AI Product Leader | 0→1 Builder & Educator | Ex-Nike, Ex-Amazon, Ex-Disney

My kids got Harry Potter Guess Who for Christmas, and watching them play gave me an idea. What if I built a digital version with our 46 family members? They could learn their relatives the same way they're learning about Hogwarts. I opened Bolt and started building. 34 versions later, I had a working game. But the real lesson wasn't about AI coding tools.

It was seeing PM fundamentals play out in real-time, compressed into one afternoon. Clear requirements prevented wrong interpretations. Defining the data model upfront avoided rewrites to the architecture. Thinking through edge cases early meant fewer fixes later. AI doesn't ask clarifying questions or push back on ambiguity. It just builds exactly what you describe.

Vibe coding is a mirror for PM discipline. It validated what I've learned across teams: clear requirements, defined data models, explicit acceptance criteria. The 4 patterns that made this work 👇

If I were teaching someone about AI product lifecycles for the first time, I'd start with POCs.
Not because they're the first step in a framework diagram.

Because they're where most AI products quietly die.

It reminds me of my research days working with Zebrafish embryos. You don't scale up a staining protocol before you've proven it works on a handful of samples.

Same logic applies to AI.

But here's what most teams get wrong: they run AI POCs like traditional software spikes. Build it, demo it, ship it.

AI doesn't work that way.

Your POC might hit 85% accuracy. Looks great in a demo. But what happens to the 15% that fails? Who handles those cases? What's the cost of being wrong?

These are the questions traditional PMs aren't trained to ask.

In my latest post, I break down:
→ Why POCs are the critical gate (not just another phase)
→ Three types of AI products and how they change what you're validating
→ The question most teams forget to ask before calling a POC "successful."

Link in comments 👇

Building your personal brand is hard. On LinkedIn, off LinkedIn—doesn't matter. It's just hard.

I have been at it for about a year now, and honestly? Still figuring it out.

I'm part of PDMA Seattle's committee, and our next session is with Leslie on how to make LinkedIn work for your PM career.

📅 Jan 14, 12-1 PM PST 🔗 https://luma.com/wno7fylk

Our last session on PM hiring trends had a great turnout, and everyone walked away with something useful. Expecting this one to be just as good.

Come through if you're trying to figure out the brand thing too.

Product Development and Management Association Seattle team - Anna Bigornia Sako Eaton Harmony Crawford Prabina B. Matt Foster Sahit G.  Banu R.

#productmanagement #linkedin #personalbrand

Open article: Stand Out With Your Brand Out. How Product Managers can grow their career using LinkedIn. · Zoom · Luma by luma.com

Stand Out With Your Brand Out. How Product Managers can grow their career using LinkedIn. · Zoom · Luma

Hi, I’m Shubham Vora.

I help business owners and entrepreneurs transform their operations using AI agents that automate tasks, save time, and scale growth.

I also share daily content about AI tools, automation tips, and agent-building workflows — practical insights anyone can apply.


Here’s what I do:
→ AI Agent Development & Consulting – Build and deploy smart agents for business automation and AI-driven SaaS applications. Helped businesses cut manpower costs by 60% and increase ROI by 5x to 20x.
→ Technical Writing – Craft content that ranks, converts, and drives inbound leads. Worked with 30+ international brands, including several Fortune 500 companies.
→ AI Video Creation – Produce short, high-impact videos for sales and training. Helped clients improve training speed by 50% and boost conversion rates through personalized video workflows.
→ LinkedIn Growth Services – Help founders and professionals grow their reach and authority. Supported creators in achieving 5x profile visibility and generating consistent inbound leads within weeks.


If you’re ready to build or scale with AI, let’s connect.

📧 Contact: +91 6352595253
🌐 Portfolio: shubhamvora.com

📅 Book a call: calendly.com/writewithshubham/30min

Shubham Vora

If you want to learn AI engineering properly, this playbook from Daily Dose of Data Science is enough.

I just finished reading this AI Engineering Playbook, and it is one of the most complete resources I have seen in one place.


Here is what is covers:

1) Core LLM Foundations
- How LLMs work under the hood
- Training stages from scratch to alignment
- Text generation, sampling, and control


2) Prompt Engineering
- Reasoning-focused prompting
- Structured prompts like JSON
- Techniques to reduce errors and drift


3) Fine-Tuning
- Why full fine-tuning fails at scale
- LoRA, GRPO, and modern tuning methods
- Creating your own tuning datasets


4) RAG Systems
- Vector databases and chunking
- RAG workflows and architectures
- Agent-based RAG and memory


5) Context Engineering
- Managing context for agents
- Different context types
- Building reliable context flows


6) AI Agents
- Agent vs LLM vs RAG
- Agent building blocks
- Multi-agent patterns and protocols


7) MCP and Agent Ops
- MCP basics and architecture
- Tool calling and server setup
- Common mistakes and fixes


8) Production Readiness
- Model optimization
- Evaluation and testing
- Deployment and observability

This playbook helped me connect theory with real system design. It is useful if you want to apply AI inside products or business processes.

Save this for later.
Repost if your team works with AI.
Follow Shubham Vora to learn more about building AI agents and using them in business workflows.

Developing Full-Funnel AI Systems and Agents for Business Growth | Tech Writing that Drives Leads | AI Video Creation for Sales & Training

AI engineering playbook

Stop guessing which agentic AI tool to use.
If you are building workflows, agents, or automation in 2026, this list matters.

Here is a clear breakdown of the Top 10 Agentic AI Tools and when to use them 👇

1. n8n
• Best for low-code workflow automation
• 1000+ integrations with strong data control
• Works well for multi-agent flows
🔗 https://n8n.io

2. Make.com
• No-code visual automation for business users
• 1,000+ SaaS integrations
• Good fit for non-technical teams
🔗 https://www.make.com

3. LangChain
• Framework to build LLM-based apps
• Strong for RAG pipelines and agent tools
• Preferred by developers building production systems
🔗 https://www.langchain.com

4. AutoGen
• Built for multi-agent conversations
• Supports human + AI collaboration
• Useful for research and complex tasks
🔗 https://lnkd.in/gXA2E4SB

5. LangGraph
• Stateful, graph-based agent workflows
• Advanced task routing and control
• Integrates well with LangChain
🔗 https://lnkd.in/gwsKmbpN

6. Flowise
• Drag and drop builder for agents
• Open source and easy to customize
• Helpful for fast RAG experiments
🔗 https://flowiseai.com

7. CrewAI
• Role-based multi-agent teamwork
• Clear task breakdown across agents
• Good for structured enterprise flows
🔗 https://www.crewai.com

8. OpenAI Agentic Stack
• Built inside the ChatGPT ecosystem
• Native tool calling and memory
• Best for quick agent rollouts
🔗 https://lnkd.in/gE3wvJcd

9. LlamaIndex
• Connects LLMs with private data
• Supports long-term memory and context
• Strong choice for enterprise RAG systems
🔗 https://www.llamaindex.ai

10. Semantic Kernel
• SDK for agent development
• Built-in memory and connectors
• Designed for enterprise systems
🔗 https://lnkd.in/gd6q4jB3


Save this post if you are working with AI agents.
Repost to help your team choose faster.
Follow Shubham Vora for practical AI, agents, and real use cases.

Scaling Business with AI Agents | AI + SaaS marketing | Lead Generation | AI Video Creation for Sales & Training

You can’t skip this.
If you want to work with AI, you must understand these 6 types of AI models first.

Most people jump straight to tools.
Strong builders start with fundamentals.


𝟭. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀
- Learn from labeled or unlabeled data
- Used for prediction, classification, and pattern finding
- Common in fraud checks, recommendations, and forecasting

𝟮. 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀
- Built using multi-layer neural networks
- Handle images, audio, video, and large text data
- Power speech systems, image recognition, and self-driving tech

𝟯. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝘀
- Create new content based on learned data patterns
- Used for text, images, audio, code, and video
- Common in chatbots, design tools, and content creation

𝟰. 𝗛𝘆𝗯𝗿𝗶𝗱 𝗠𝗼𝗱𝗲𝗹𝘀
- Combine multiple AI techniques in one system
- Mix rule-based logic with learning models
- Used where accuracy, control, and reliability matter

𝟱. 𝗡𝗟𝗣 𝗠𝗼𝗱𝗲𝗹𝘀
- Understand and process human language
- Used in chatbots, search, translation, and summaries
- Core of most text-based AI systems

𝟲. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀
- Read and analyze images and videos
- Used for object detection and visual inspection
- Common in healthcare, retail, and security systems

AI is not one thing.
It is a stack of models solving different problems.

Save this post for reference.
Repost to help others learn.
Follow Shubham Vora to learn how to apply AI in real business and development work.

Thanks to Shalini Goyal for this image.

If you want to learn AI automation from zero, this 18-day roadmap is enough.

No theory overload. No random tools. Just step-by-step skills that actually help you build real automations.


Here is what each day covers:
Day 1: Basics of AI automation and how it is used in real business
 Day 2: Automation thinking and how to break work into clear steps
 Day 3: APIs and webhooks for app to app communication
 Day 4: No code tools like Make, Zapier, and n8n
 Day 5: Build your first simple automation
 Day 6: Handle data like text, numbers, and dates
 Day 7: Logic, conditions, retries, and error handling
 Day 8: AI model basics and how LLMs work
 Day 9: Use AI inside automations the right way
 Day 10: Prompt design for stable automation outputs
 Day 11: Automate emails, notes, and text tasks
 Day 12: RAG basics to connect AI with real data
 Day 13: AI agents basics and where they fit
 Day 14: Business use cases like support and reports
 Day 15: Sales and marketing automation with AI
 Day 16: Operations automation for daily work
 Day 17: Monitor, fix, and improve automations
 Day 18: Build and ship a complete automation system

If you spend one focused hour each day, you can go from beginner to builder in less than three weeks.

Save this post for later and follow Shubham Vora for more content on AI, automation, and development.

Image credit: Greg Coquillo

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The Battle

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Shaili Guru's LinkedIn Performance

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Performance ScoreShaili Guru's LinkedIn Performance