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

Entrepreneur | Growth Systems Builder

Hi, I’m Shubham Vora.

I help businesses grow using AI agents, AI-driven SaaS marketing, and AI-based lead generation systems that reduce manual work and improve results.

I also share practical content on AI tools, automation, SaaS marketing, lead generation, and agent workflows that teams can apply without heavy tech setup.


What I do:
→ AI Agent Development and Consulting  
→ AI SaaS Marketing  
→ AI Lead Generation  
→ Technical Writing  
→ AI LinkedIn Growth Services
→ AI Video Creation for Sales & Training and for Founders

If you are building, scaling, or fixing growth gaps using AI, let’s connect.

📧 Contact: +91 6352595253 
📧 Email: sv@nutsovertech.com  
🌐 Portfolio: nutsovertech.com
📅 Book a call: calendly.com/writewithshubham/30min

Shubham Vora

Most AI agent developers think prompt engineering is just “write a better prompt.”

It is much deeper than that.

I recently went through a detailed PDF on Prompt Engineering Techniques, and it explains how serious AI builders structure prompts for reliable outputs.

If you are building AI agents, LLM apps, or automations, this knowledge is extremely useful.


What the PDF Covers:

1. Core Prompt Foundations
• Why prompt engineering matters for accuracy
• How context improves AI responses
• Structuring prompts for better outputs


2. Important LLM Parameters
• Temperature and how it controls creativity
• Token limits and output length
• Frequency and presence penalties to reduce repetition


3. Prompt Engineering Frameworks
Structured frameworks to guide AI clearly:
• RTF → Role, Task, Format
• CTF → Context, Task, Format
• RTSCEF → Role, Task, Steps, Context, Examples, Format
These frameworks make prompts predictable and easier to reuse.


4. Advanced Prompting Techniques
Used in complex AI systems:
• Chain of Thought reasoning
• Zero-shot and Few-shot prompting
• ReAct (reasoning + actions with tools)
• Tree of Thought problem solving
These techniques improve reasoning and reduce wrong answers.


5. Tools for Building LLM Systems
• LangChain for building AI workflows
• Guidance for structured prompt execution


If you want to build:
• AI agents
• LLM products
• AI automation systems
You need structured prompting, not random prompts. This PDF explains the foundations clearly.


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Turning Businesses into Industry Giants with End-to-end AI Solutions | AI + SaaS marketing | Lead Generation | AI Video Creation for Sales & Training

Prompt engineering techniques

52 pages. That is all you need to revise Machine Learning properly.

No need to jump between 10 videos or random blogs.

I came across this Stanford and MIT-level AI cheatsheet, and it is one of the most practical resources I have seen for anyone working in AI.


Here is what it covers and why it matters:

1. Strong foundation (no shortcuts)
→ Probability, Bayes rule, distributions, and statistics
→ Core math like linear algebra and matrix calculus
→ Helps you understand why models behave the way they do

2. Machine Learning that actually works in real life
→ Regression, SVM, Naive Bayes, k-NN
→ Clustering and PCA for data analysis
→ Regularization and cross-validation to avoid bad models
→ Metrics like Precision, Recall, F1, ROC

3. Deep Learning simplified
→ Neural networks, backpropagation, activation functions
→ CNNs for images, RNNs for sequences
→ Concepts like dropout, Adam optimizer

4. AI concepts beyond models
→ Search algorithms like BFS, DFS, A*
→ Reinforcement Learning with Q-learning
→ Game logic like Minimax
→ Bayesian networks and logic systems


Now the best part.
This is not just theory.

It connects directly to real use cases:
→ Spam detection using Naive Bayes
→ Object detection using YOLO and R-CNN
→ Face recognition with Siamese networks
→ NLP tasks like translation and sentiment analysis
→ Generative AI with GANs
→ Game agents using Minimax

If you are:
→ Preparing for AI interviews
→ Building AI products
→ Or want clarity instead of confusion

This single cheatsheet can save you hours every week.

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ML cheatsheet

$2000+ worth of AI learning resources are free. Microsoft-backed. Certified. And you can start today.

If you want to build AI skills, improve productivity, or move into AI roles, this list is a strong starting point.


Here are the 9 programs you should not miss:

1. Career Essentials in Generative AI
Learn core generative AI concepts and career-focused practical foundations.
https://lnkd.in/dMakEgKZ


2. Generative AI Productivity Skills
Use AI tools to improve daily work output and efficiency.
https://lnkd.in/dRb_Gj_n

3. AI for Beginners (12-Week Roadmap)
Structured roadmap covering AI basics with practical coding examples.
https://lnkd.in/dxWw8XVT

4. Transform Your Business with Microsoft AI
Understand AI use cases for growth, strategy, and operations.
https://lnkd.in/d5pCyFC5

5. AI for Managers
Guide for leaders to adopt AI responsibly and strategically.
https://lnkd.in/db5dd_SP

6. Using Azure OpenAI Service
Build and deploy AI solutions using Azure OpenAI tools.
https://lnkd.in/dDaEUWDz

7. Build Apps with Azure & Power Virtual Agents
Create AI-powered apps and chatbots with Microsoft tools.
https://lnkd.in/d2n76Nkw

8️. Create Custom Machine Learning Models
Train, evaluate, and deploy machine learning models step-by-step.
https://lnkd.in/dxAGnESJ

9️. Adopting Copilot for Microsoft 365
Implement Copilot to improve collaboration and business workflows.
https://lnkd.in/dpHexpEJ


AI skills are no longer optional.

Start small. Stay consistent. Build real capability.

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Anthropic just dropped FREE AI courses with certificates that may be more useful than many paid AI courses.

Their new learning platform covers AI fluency, Claude usage, APIs, MCP, and agent-style workflows. The courses are hosted on their academy and designed for beginners, developers, and teams.


Here are the courses you can take right now:

1. Claude Code in Action
Learn how to use Claude Code to read files, execute commands, and integrate AI into your development workflow.
https://lnkd.in/dVR_U4sm


2. Claude 101
A beginner course that shows how to use Claude for everyday work tasks and understand its core features.
https://lnkd.in/dsfzWmYC


3. AI Fluency: Framework & Foundations
Learn how to collaborate with AI systems effectively, responsibly, and safely using the AI Fluency framework.
https://lnkd.in/dUW9H9V4


4. Building with the Claude API
A developer course covering how to build applications using Claude’s API and production AI patterns.
https://lnkd.in/dcUJbavj


5. Introduction to Model Context Protocol (MCP)
Learn how MCP connects Claude with external services and how to build MCP servers and clients.
https://lnkd.in/d9XxpDBC


6. Model Context Protocol: Advanced Topics
Deep dive into MCP features like sampling, notifications, transports, and production server patterns.
https://lnkd.in/dKfrNUFa


7. Introduction to Agent Skills
Learn how to create reusable “skills” so Claude can perform repeated tasks automatically.
https://lnkd.in/dEF55uag


8. Claude with Amazon Bedrock
Build applications using Claude models through AWS Bedrock APIs and cloud workflows.
https://lnkd.in/dJubKb-M


9. Claude with Google Cloud Vertex AI
Learn how to access and deploy Claude models using Google Cloud Vertex AI infrastructure.
https://lnkd.in/d-YeimwB


10. AI Fluency for Students
Develop AI fluency skills for learning, career planning, and responsible AI collaboration.
https://lnkd.in/dnaFyceR


11. AI Fluency for Educators
Guidance for educators to integrate AI fluency into teaching and institutional strategy.
https://lnkd.in/dDXqCZsJ


12. Teaching AI Fluency
Learn methods to design assignments and teaching approaches for AI-enabled education.
https://lnkd.in/dWT2k2tm

AI is moving fast. The people who learn agent workflows, APIs, and AI collaboration now will stay ahead.


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Start using OpenClaw to handle your daily repeat tasks and cut thousands in costs.

I watched this video from Metics Media and it’s perfect if you’re just getting started.

Here is the link: https://lnkd.in/dr5pGrkk


Here’s what it covers:
→ What OpenClaw is and why it’s different
→ Full VPS setup without writing code
→ Security setup before connecting any tools
→ Actual cost breakdown so you plan better
→ Telegram bot setup with voice support
→ Google Workspace integration (Gmail, Calendar, Drive)
→ Memory system and identity setup
→ Daily automation using cron jobs
→ Smart model routing to reduce cost
→ Free LLM fallback setup
→ Sub-agents for parallel execution
→ Recovery steps and system updates

If you want to build AI agents that actually work in real scenarios, this is a solid starting point.

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Most developers try Claude Code once and think it is inconsistent.

In most cases, the issue is not Claude.

It is the setup.

Here is a simple way to configure Claude Code so it works smoothly in real projects.

Step 1. Install Claude Code globally
→ Open your terminal
→ Run: npm install -g @anthropic/claude-code
→ Make sure Node.js version is 18 or above
→ Generate your API key from console.anthropic.com
→ Run claude to sign in and begin using it


Step 2. Start Claude from your project folder
→ Avoid running Claude from the home directory
→ First move to your project: cd your-project
→ Then run the claude command
→ If the folder is wrong, Claude receives the wrong context


Step 3. Add a CLAUDE.md file
→ This file acts as the project memory for Claude
→ Create it in the root directory of your repository
→ Claude reads this file before every session
→ Without this file, Claude starts each session without context


Step 4. Organize the CLAUDE.md file properly
→ Write a short overview of the project in a few sentences
→ List the complete tech stack with version details
→ Define naming standards and coding practices
→ Mention files or folders Claude should avoid modifying


Step 5. Use /init when starting a new project
→ Run /init the first time you open Claude in a repo
→ Claude scans the repository automatically
→ It prepares a draft version of CLAUDE.md
→ Review it, adjust it, and keep it aligned with your project


Step 6. Give Claude full execution instructions
→ Avoid asking Claude to only write code
→ Ask it to write the code, run tests, and fix issues
→ Provide one clear instruction covering the full workflow
→ This approach improves speed and reliability


Step 7. Connect MCP servers
→ Claude Code supports external MCP integrations
→ Connect systems such as databases, APIs, and documentation
→ This allows Claude to work with your live environment
→ The additional context improves the results significantly


Step 8. Divide tasks into smaller checkpoints
→ Requests like “build authentication system” are too broad
→ Instead break the task into smaller steps
→ For example: create login route, then implement session logic
→ Smaller tasks usually produce better output


Step 9. Ask for the plan before execution
→ Request Claude to show the plan first
→ Review the proposed steps carefully
→ Approve the approach before changes are applied
→ This habit helps avoid many mistakes


Before starting your next session, check these three things

→ Are you inside the correct project directory?
→ Does your CLAUDE.md file exist and stay updated?
→ Is your task small enough to iterate quickly?

Set these basics first.

Then start coding.

If this was useful:

→ Save this post for later
→ Repost so other developers can use it
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AI agents are becoming a core layer in modern software.
But building reliable agents depends heavily on the frameworks and platforms you choose.

Here are 12 platforms developers use to build AI agents today:

𝟭. 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻
• Popular framework for building LLM apps with memory, tools, and orchestration
• Use it for building complex agent workflows and production AI applications
• https://www.langchain.com


𝟮. 𝗔𝘂𝘁𝗼𝗚𝗣𝗧
• Autonomous AI agent that can plan and execute tasks independently
• Use it for experimenting with self-operating AI workflows
• https://lnkd.in/gCkjESvh


𝟯. 𝗔𝗴𝗲𝗻𝘁𝗚𝗣𝗧
• Browser-based interface to build and run autonomous agents
• Use it if you want to create AI agents without heavy coding
• https://lnkd.in/g8vYHmBE


𝟰. 𝗖𝗿𝗲𝘄𝗔𝗜
• Framework for building role-based collaborative AI agents
• Use it for multi-agent systems where agents act like a team
• https://lnkd.in/gu89XcbW


𝟱. 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗔𝘂𝘁𝗼𝗚𝗲𝗻
• Framework that enables structured communication between multiple AI agents
• Use it for building complex agent conversations and enterprise workflows
• https://lnkd.in/gXA2E4SB


𝟲. 𝗦𝗺𝗼𝗹𝗔𝗴𝗲𝗻𝘁𝘀
• Lightweight agent library from Hugging Face for fast development
• Use it for quick AI agent prototypes and small experiments
• https://lnkd.in/gcQwZ_7t


𝟳. 𝗠𝗲𝘁𝗮𝗚𝗣𝗧
• Open-source framework designed for complex multi-agent systems
• Use it to simulate software teams like PM, developer, and tester agents
• https://lnkd.in/gYBqRJcH


𝟴. 𝗦𝘂𝗽𝗲𝗿𝗔𝗚𝗜
• Developer-focused framework for building autonomous agents
• Use it to create scalable AI agent infrastructure
• https://lnkd.in/gtM5HBN4


𝟵. 𝗻𝟴𝗻
• Workflow automation tool that integrates AI with business processes
• Use it to build AI-powered automation workflows without heavy coding
• https://n8n.io


𝟭𝟬. 𝗕𝗼𝘁𝗽𝗿𝗲𝘀𝘀
• Conversational AI platform for building chatbots and AI agents
• Use it for customer support automation and chat interfaces
• https://botpress.com


𝟭𝟭. 𝗥𝗮𝘀𝗮
• Open-source framework focused on natural language understanding
• Use it to build context-aware conversational assistants
• https://rasa.com


𝟭𝟮. 𝗠𝗮𝘀𝘁𝗿𝗮
• Open-source TypeScript framework for building AI agents and workflows
• Use it if you build AI applications using the JavaScript ecosystem
• https://mastra.ai

AI agents are quickly becoming a major part of software development.

If you found this useful:
• Share this post with your network.
• Follow for more insights on AI and software development.
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Most people think becoming an AI Engineer means training AI models.

That is not true.

I watched a full video from Marina Wyss (Applied ML at Amazon) about how to get your first AI engineering job.

Here is the link: https://lnkd.in/degHSTZv

It covers:

1. What AI Engineers actually do
They usually do not train models.
They build applications using existing models like GPT or Llama.

Common work includes:
- Prompt engineering
- RAG systems
- AI agents
- Fine-tuning models
- Building real AI products


2. Skills you need
Foundations:
- Python
- Git
- APIs
- Basic ML concepts

Core skills:
- OpenAI or LLM APIs
- Prompt engineering
- RAG applications
- AI agents
- Docker + cloud deployment


3. Portfolio matters more than courses
Course projects rarely help.

Better approach:
- Build real projects
- Use real data or APIs
- Create full AI applications with UI and deployment

Best case:
- Build something for a real client or organization.


4. Resume tip
Do not write:
“Aspiring AI Engineer”

Write something like:
AI Engineer building LLM apps, RAG systems, and AI agents


5. Networking tip
Instead of asking for jobs:
- Read a company’s AI work
- Ask thoughtful questions
- Start a conversation
- This works better than sending resumes blindly.


6. Realistic timeline
- Getting job ready usually takes:
- 6 months for basics
- 6–12 months for advanced skills
- 1–2 years to reach professional level
- AI engineering is a serious skill, not a quick shortcut.


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Claude Code is not just an AI coding assistant. With hooks, it becomes a system that controls how code is written, tested, and shipped.

This PDF shows how to use Claude Code the right way.


Here’s what it covers:

1. Hooks that actually control Claude
→ Block risky commands like delete or unsafe Docker runs before execution
→ Force test creation when new code is written
→ Auto-format code right after edits
→ Add logs so every action is tracked

2. Smarter workflows with hooks
→ Run Python formatters only when Python files change
→ Open your IDE directly where Claude made edits
→ Auto-stage and prepare commits after changes
→ Run fixes inside CI/CD without manual steps

3. Slash commands that save time daily
→ Review PRs in seconds with a single command
→ Run security checks without writing long prompts
→ Fix GitHub issues with one structured input
→ Standardize how your team uses Claude

4. MCP integrations that extend Claude
→ Connect Claude directly with GitHub actions
→ Use browser automation for testing flows
→ Pull official docs while coding
→ Track every external action for audit

5. Sub-agents that work like a team
→ Create a test writer agent for coverage
→ Use a security agent to scan vulnerabilities
→ Chain agents to debug, fix, and review code
→ Get focused outputs instead of mixed responses

6. Fix where AI usually fails
→ Stop Claude from jumping straight into coding
→ Add architecture checks before implementation
→ Enforce test-first and validation rules
→ Bring structure into every change

7. Step-by-step adoption
→ Start with simple automation and logs
→ Add safety checks and formatting rules
→ Move to workflow automation
→ Build multi-agent systems
→ Scale to full engineering processes

If you're using Claude casually, you're missing most of its value.

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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);}Shubham Vora's LinkedIn Performance

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