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explainx / curriculum sample

LLM ops, evaluation & research curriculum▌

For ML platform and research adjacent teams—expects quantitative literacy.

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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

  • Build eval suites spanning offline, online, and human preference layers.
  • Discuss fine-tuning vs. RAG vs. prompt-only with total-cost framing.

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

Evaluation architecture

Beyond leaderboard chasing.

session outline

  • Task suites
  • Judge models
  • Human calibration

labs

  • Design minimal harness

beyond-catalog topics (custom)

  • Synthetic data pitfalls in highly regulated verticals

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

Evaluating AI Agents

Learn to Evaluate AI Agents Rigorously: Benchmarking Accuracy, Reliability, and Safety with Automated Test Harnesses and Evaluation Frameworks

Ollama Zero to Hero: Build Chat, Vision Games & AI Agents

Run LLMs Locally with Ollama: Build Chat Apps, Vision Projects, Games, and AI Agents on Your Own Hardware — No Cloud Required

DeepSeek R1: Build AI Agents & RAG Apps on Your Own Machine

Run DeepSeek R1 Locally with Ollama: Build RAG Applications, AI Agents, and Full-Stack AI Apps Without Cloud Dependencies

faq

GPU budget discussions?

We can integrate CapEx/OpEx framing with your FinOps partners when invited.

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