Training Materials training

AI courses for consultants, engineers, and anyone who wants to use AI tools well without getting fired.

- bitsmasher.net/training

Core Modules

Module 01 — Internal Tools Overview DRAFT

PAN internal AI tools: Notebook LM, Gemini, Panda AI, chat.pan.dev, #ai-tips Slack. What's current, what's deprecated, how to choose the right tool for the task.

By the end:
  • Identify which AI tools are appropriate for a given consulting scenario
  • Configure PAN-approved AI tools per policy requirements
  • Demonstrate tool selection with a real-world example

Includes internal tools inventory, deprecation timeline, and decision flowchart. Speaker notes + quiz sections planned.

Module 02 — Security & Data Classification DRAFT

PAN's approved AI tools list with approval context. Data classification rules for AI use. Incident response procedure for improper AI usage.

By the end:
  • Categorize data by sensitivity level and determine if it can enter any AI system
  • Navigate PAN's approved tools registry and justify tool selection against policy
  • Demonstrate incident response workflow for improper AI usage

Expanded security section (~4 slides), data classification decision tree, approved tools matrix. Cheat sheet available as take-home PDF.

Module 03 — Model Context Protocol DRAFT

What MCP is, how it works under the hood, current spec status and implementations (Aug 2026). Architecture diagrams with up-to-date components. PAN-specific usage patterns.

By the end:
  • Explain the MCP architecture and its role in tool integration
  • Evaluate current MCP implementations against project requirements
  • Demonstrate a basic MCP server/client connection with a local model

Architecture diagrams updated, PAN policy references included. Source links verified against Aug 2026 spec.

Module 04 — Prisma Cloud + AI (AIRS) DRAFT

Prisma Cloud's AI capabilities: threat detection, risk scoring, consultant use cases. PAN-specific policies and practical scenarios for customer engagements.

By the end:
  • Identify Prisma Cloud AI features relevant to consulting workflows
  • Walk through a threat detection scenario using AI-powered risk scoring
  • Apply PAN-approved usage guidelines in a customer-facing context

PAN policies, approved usage guidelines, practical customer scenarios. Deck + PDF compiled and ready.

Planned Modules

Module 05 — AI Security for LLMs PLANNED

OWASP Top 10 for LLMs, prompt injection defense, output validation. For the people who build AI systems rather than just use them.

Module 06 — Prompt Engineering Deep Dive PLANNED

Advanced techniques: system prompts, few-shot patterns, chain-of-thought. When to use which approach and why it matters in consulting engagements.

Hands-On Labs

Lab 01 — Prompt Engineering PLANNED

Write, test, and iterate prompts for a specific task. Measure effectiveness across different approaches.

Lab 02 — Token Counting PLANNED

Measure token consumption across different inputs and approaches. Learn to estimate costs and optimize prompts.

Lab 03 — Simple RAG Pipeline PLANNED

Build a minimal viable retrieval-augmented generation pipeline: vector DB + embeddings + LLM.

Lab 04 — MCP Server & Client PLANNED

Set up a local MCP server and client with an open-source model. Verify connectivity and message flow.

Labs include: objective, prerequisites, step-by-step instructions, and verification checklists. Each takes ~1-2 hours.

Resources

📄 Coding Foundations (HTML) — interactive reference sheet

📄 AI Security Primer (HTML) — OWASP Top 10 for LLMs reference

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