explainx.ainewsletter3.02k

explainx / curriculum sample

Engineering & IT AI curriculum — SDLC, security, platform▌

For Eng/IT—pairs with our agent-skills direction; heavier on dev workflows and platform abstractions.

Discuss this track →Browse program pages

instructional design: bloom’s taxonomy + measurable outcomes

Every module maps to explicit learning outcomes—not open-ended discussion without deliverables. We sequence along Bloom’s taxonomy (remember → understand → apply → analyze → evaluate → create): definitions and guardrails first, then applied exercises, then measurement and approvals. Facilitators run short checks for understanding after each block (2026 materials).

For organic and generative-engine visibility (GEO), we mirror patterns associated with stronger AI-search citation: answer-first sections, statistics where available, authoritative tone, clear H1–H3 structure, comparison tables when they reduce ambiguity, and FAQ blocks intended to pair with FAQPage JSON-LD. Teams produce briefs, scorecards, and checklists—not a generic “AI creativity” workshop.

program objectives

  • Standardize approved IDEs, copilots, and internal package registries.
  • Define how code-assist tools interact with PR review gates.

how we deliver

  1. 1

    Discovery call & problem framing

    We align on sponsors, success metrics, and constraints (2026 tool landscape, data rules, procurement gates) before anything is scheduled company-wide.

  2. 2

    Stakeholder interviews & day-in-the-life context

    Short conversations with practitioners (not only leadership) so scenarios reflect real workflows—not generic slide demos.

  3. 3

    Curriculum design & artifacts

    Modular agenda, exercise scripts, evaluation rubrics, and governance checkpoints matched to your vocabulary (banking, FMCG, engineering, etc.).

  4. 4

    Engaged, hands-on delivery

    Facilitation-led sessions with live exercises, breakout prompts, and documented failure modes—minimum passive lecture time.

  5. 5

    Post-session support: documentation & next steps

    Written recap, pilot backlog, links to explainx.ai courses for scaled upskilling, and optional office hours so momentum doesn’t stop at the workshop.

modules

Secure SDLC hooks for AI assists

So speed doesn’t bypass checks.

session outline

  • Secret scanning
  • Dependency policy
  • CI gates

labs

  • PR checklist addendum draft

beyond-catalog topics (custom)

  • Inner-source policy when multiple repos adopt different agent tools

quick contact

Scope or pilot this curriculum

Share sponsor, headcount, and cities — we reply with timing and options. Rough budget helps us match the right depth.

related on-demand courses

Agent Skills: Claude Code, Cursor and MCP in Practice

Ship Agent Skills, Claude Code Workflows, and MCP Integrations: Hands-on Training for SKILL.md Authoring, Cursor Productivity, and MCP Server Projects

Intro to OpenAI Codex: Fully Agentic Coding

Master OpenAI Codex CLI: From Setup to Agentic Coding Workflows, Automating Software Development, and Building Production-Ready AI Apps

Vibe Code a Chrome Extension with Cursor AI

Build a Real Chrome Extension Using Cursor AI: Natural Language to Working Extension, Vibe Coding Methodology, Publishing and Iterating with AI Assistance

faq

Platform vs. application teams?

Split modules are possible—we often run two tracks with shared governance layer.

← All curriculum samples·training hub