From AI Power User to Strategic Unit/Org Leader in 6 Weeks
This program is designed specifically for you, Vishal — an engineer with strong analytical skills but no software development background. The focus is on making you an AI power user and equipping you with the knowledge to drive unit/organization AI adoption with informed executive decisions.
Master AI tools for document analysis, briefing automation, decision support, and workflow efficiency
Understand governance frameworks, risk management, and how to approve/reject AI initiatives responsibly
Learn prompt injection, hallucinations, data leakage risks — aligned with OWASP and NIST frameworks
Build automations and agents without programming (Python is optional, in appendix)
Each week builds on the previous one. You'll go from understanding Gen AI basics to deploying automations, then to making strategic decisions using recognized risk frameworks (NIST AI RMF 1.0). By Week 6, you'll be ready to lead AI adoption in your unit.
Get instant gratification — build & deploy a web app in under 1 hour (no code!)
Experience the power of AI-assisted development by building a complete To-Do app using natural language prompts. This creates momentum and shows you what's possible with Gen AI tools.
Build and deploy a live To-Do app using only plain language prompts. No syntax, no frameworks — just describe what you want and watch it appear. You'll learn prompting, code review basics, and one-click deployment.
A live, deployed To-Do web app with a shareable URL. You'll have experienced the full cycle: prompt → code generation → testing → deployment, all in natural language.
Each week is designed to take 6–8 hours. Follow the order; each builds on the previous.
Understand what GenAI can/can't do; build strong prompting habits early
By the end of Week 1, you will understand the core concepts of generative AI, how LLMs work, and how to write effective prompts. You'll be able to articulate what Gen AI is good at (summarization, drafting, Q&A) and where it fails (accuracy, reasoning).
Andrew Ng's non-technical introduction to Gen AI. Covers how it works, what tools exist, AI strategy for work/business, and advanced use beyond prompting. No coding required.
Best practices for prompt engineering taught by Isa Fulford (OpenAI) and Andrew Ng. Covers guidelines, iterative refinement, summarizing, inferring, transforming text. Includes 7 code examples (which you can just read, no need to code).
Quick systems view of Gen AI building blocks: LLMs, prompt techniques, AI agents. Good for reinforcing Week 1 concepts from a slightly different angle.
1-page shortlist: Write down 5 work use cases where Gen AI could help you (e.g., drafting briefings, summarizing reports, decision support, automating emails, Q&A on SOPs). For each, note: input, desired output, and which Gen AI capability applies (summarization, drafting, search, automation).
Become an AI power user for docs & briefs — without being fooled by confident errors
Master practical AI workflows for document analysis and briefing automation. Critically, learn to recognize and mitigate hallucinations, understand responsible AI principles, and know when NOT to trust AI outputs.
Google's curated path covering Gen AI concepts, large language models, and responsible AI principles. Last updated recently. Excellent for practical, real-world context.
Micro-learning course on what responsible AI is, why it matters, and how to implement it. Earn a badge upon completion.
Short modules on responsible AI best practices, principles, deepfakes, copyright, and global implications. Reinforces Google's content from a different perspective.
What it is: AI models generate plausible-sounding but factually incorrect information with high confidence.
Mitigation: Always verify critical facts. Use RAG (Week 4) to ground responses in trusted documents. Add a "verification plan" step to every AI-generated output.
Briefing Workflow Template: Create a repeatable process for long PDFs:
1. Summarize: Get the main points
2. Extract claims: Identify key assertions
3. List unknowns: What's missing or unclear?
4. Verification plan: How will you check the facts?
Test this on a real document (non-sensitive) and document the results.
Build practical automations while learning what can go wrong with tool-using agents
Build working no-code automations (email → summarize → log) using n8n. Understand prompt injection attacks at a practical level and why tool permissions matter.
Official documentation for building AI agents in n8n. Learn the core concepts of workflows, nodes, and AI integration. This is your reference guide.
Fast, hands-on video tutorial to get your first n8n AI agent running. Perfect for visual learners.
Essential security knowledge: understand how malicious instructions can be hidden in user text or web content to steer the AI. This is the #1 risk for agent deployments.
Prompt Injection: Malicious text can trick AI agents into performing unintended actions.
Mitigation for Week 3:
Automation Design Document: Design (not build) an automation workflow on paper:
• Use Case: Pick one task you want to automate (e.g., "Summarize daily briefing emails")
• Input: What triggers the automation? (e.g., "New email in specific folder")
• AI Processing: What should AI do? (e.g., "Summarize, extract action items")
• Output: Where should results go? (e.g., "Send summary to Slack")
• Failure Mode: What could go wrong? (e.g., prompt injection, hallucinations)
Draw a simple flowchart showing: Trigger → AI Process → Output → Human Review. You can build this later if you want, but understanding the design is the goal.
The most useful executive pattern: grounded Q&A over trusted documents
Learn Retrieval-Augmented Generation (RAG) — the technique that lets AI answer questions based on YOUR documents (policies, SOPs, briefings). This reduces hallucinations and provides citations.
Full walkthrough of Retrieval-Augmented Generation by a LangChain engineer. Covers indexing documents, vector search, retrieval, and generating grounded answers. Even if you don't code, watch to understand the RAG pipeline.
Collection of practical RAG patterns and examples. Use this as a reference when you want to see specific implementations (e.g., RAG with metadata, RAG with images).
RAG is the difference between "AI making stuff up" and "AI answering based on YOUR authoritative documents." For unit/org adoption, RAG lets you:
RAG Experiment using GPT-5.3 Custom GPTs or Claude Sonnet 4.6 Projects: Use the built-in "Custom GPT" feature in ChatGPT or "Projects" in Claude (no coding needed):
Step 1: Upload 2-3 documents (PDFs) to a Custom GPT or Claude Project (use non-sensitive public docs like SOPs, guides)
Step 2: Ask 5 questions that should be answerable from those docs
Step 3: For each answer, check:
• Did it cite the right document/section?
• Is the answer accurate?
• Did it admit when info wasn't in the docs?
Step 4: Write a 1-page summary:
• What worked well? (grounding, citations)
• What failed? (hallucinations, missing context)
• Where would RAG help in your unit? (use cases)
This teaches RAG concepts without coding. Works with ChatGPT Plus (GPT-5.3), Claude Pro (Sonnet 4.6), or Gemini Advanced.
Understand agentic AI + high-level security literacy for approving initiatives
Learn what AI agents are (autonomous systems that use tools, make decisions, and take actions). Gain executive-level security literacy via OWASP Top 10 for LLM Applications 2025.
Free, structured course on AI agents from basics to building your own. Covers agent fundamentals, frameworks, use cases, and includes a final assignment. Earn a certificate. Basic Python + basic LLM understanding required (you have this from Weeks 1-4).
Quick intro to LangChain for agent building. Optional reinforcement if you want to see agent frameworks from another angle.
The industry-standard security checklist for Gen AI applications. Read to gain high-level literacy on the 10 critical risks: Prompt Injection, Sensitive Info Disclosure, Supply Chain, Overreliance, Excessive Agency, and more.
Don't read it like a textbook. Instead, skim each of the 10 categories and write 1–2 lines:
Example: LLM01 Prompt Injection → "Attacker hides malicious instructions in user text" → "We use instruction hierarchy, tool allowlists, and human approval for high-impact actions."
AI Agent Approval Checklist (1 page): Create a checklist you'd use to approve/reject an agent initiative in your unit. Include:
• Data access: What data does the agent need? Is it sensitive?
• Tool permissions: What actions can it take? (read-only, write, execute, API calls)
• Human-in-the-loop: Which actions require human approval?
• Logging: How do we audit agent actions?
• Failure modes: What happens if the agent gets prompt-injected or hallucinates?
• Rollback plan: How do we disable or revert the agent quickly?
Base this on the OWASP categories (especially LLM01, LLM02, LLM09, LLM10).
Make informed executive decisions using NIST AI RMF 1.0 + optional voice/local models
Master the NIST AI Risk Management Framework (Govern, Map, Measure, Manage) to make strategic decisions about AI adoption. Optionally explore voice agents and local models for awareness.
The gold-standard voluntary framework for AI risk management. Published by the US National Institute of Standards and Technology. Read the Executive Summary and the four core functions: Govern, Map, Measure, Manage.
Use this as your executive decision framework:
OpenAI's research on Instruction Hierarchy — a technique to help LLMs prioritize trusted instructions over untrusted user input. High-level but important for org adoption.
Anthropic's research on defending browser-based AI agents from prompt injection. Includes practical mitigation strategies.
Learn to run LLMs locally on your machine (privacy, control, no API costs). Good awareness for data-sensitive deployments.
Short course on voice agent architecture, STT/TTS/LLM components, latency trade-offs, and cloud deployment. Good awareness for conversational AI initiatives.
GenAI Risk Register (1 page): Create a risk register for your unit/org context. For each risk category (see next section), document:
• Risk name (e.g., "Hallucinations in briefings")
• Impact (Low/Medium/High/Critical)
• Likelihood (Rare/Possible/Likely)
• Mitigation (what you'll do about it)
• Owner (who's responsible)
Use the NIST functions as your framework and OWASP Top 10 as your risk source.
Use this as your minimal vocabulary set for risk discussions. No deep technical work needed — just enough to ask the right questions and approve/reject initiatives.
Model produces plausible but factually incorrect information with high confidence.
Malicious instructions hidden in user text or web content steer the model to perform unintended actions.
Pasting confidential data into public AI tools; retention/logging concerns; model memorization.
Humans stop thinking critically and accept AI outputs without verification.
Agents have too much tool access without proper guardrails, leading to unintended consequences.
Risky plugins, models, templates from unvetted sources; licensing issues; backdoors.
Use this sample checklist as a starting point to evaluate AI initiatives in your unit. Customize it based on your organization's specific policies, security requirements, and operational context. Aligned with NIST AI RMF 1.0 (Govern, Map, Measure, Manage).
This checklist is provided as an example framework based on NIST AI RMF 1.0 and OWASP Top 10 for LLMs. Adapt it to your unit's specific requirements, security policies, data classification levels, and operational procedures before use.
These are optional extensions. Only pursue if you have extra time or specific interest.
Only needed if you want to read/modify AI code examples or build custom scripts.
7 lessons covering Python syntax, functions, lists, loops, strings. Interactive coding exercises.
Start CourseBuild apps using AI assistants (Lovable, Bolt, Cursor) without traditional coding. Great for rapid prototyping.
Build a subscription app from scratch with payments, user accounts, and deployment.
Start CourseBuild a full-stack app in 30 minutes using AI. From idea to deployment.
Watch TutorialBeginner-friendly intro to vibe coding and no-code app building with AI.
Watch VideoOnly if you want more formal training beyond NIST AI RMF.
If you loved Week 4 and want to go deeper into production RAG systems.
LangChain + LlamaIndex + RAG + 7 industry projects. Free certificate.
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