explainx.ainewsletter3.02k

explainx / curriculum sample

RAG & retrieval curriculum — grounded answers at enterprise scale▌

Retrieval is where many ‘ChatGPT for documents’ programs quietly fail. This track emphasizes offline evals, chunking strategy, and operator feedback loops over embedding hype.

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

  • Choose chunking and metadata strategies aligned to ticket volume and doc heterogeneity.
  • Define truthiness tests and human QA sampling rates before production readouts.
  • Understand when to re-rank vs. expand queries vs. switch retrievers entirely.
  • Integrate analytics that tie retrieval quality to business KPIs (resolution time, after-contact work).

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

Designing the knowledge substrate

Docs, wikis, PDFs, and structured stores—normalization plan first.

session outline

  • Content inventory: authoritative sources vs. stale mirrors.
  • Metadata contracts: ACLs, freshness, ownership.

labs

  • Sketch chunk boundaries for two real document types you bring (anonymized).

beyond-catalog topics (custom)

  • Hybrid lexical + dense retrieval pairings for regulated vocabularies.
  • Handling tables, scanned PDFs, and multi-language corpora common in India + Middle East rollouts.

Eval harness & continuous improvement

What to measure weekly.

session outline

  • Offline vs. online metrics; when A/B tests mislead in low-traffic B2B settings.
  • Human labeling throughput vs. spot checks—a pragmatic hybrid.

labs

  • Draft a minimal eval rubric with acceptance thresholds.

beyond-catalog topics (custom)

  • Routing policies when user intent is ambiguous (multi-intent enterprise search).

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

Basic to Advanced: Retreival-Augmented Generation (RAG)

Multi-modal RAG Stack: A Hands-on Journey Through Vector Stores, LLM Integration, and Advanced Retrieval Methods

Langflow Mastery: Build Advanced AI Workflows

Master Langflow: Build RAG Pipelines, Multi-Agent Flows, and Production AI Workflows with a Visual No-Code/Low-Code Builder

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

Generative AI for Leaders & Business Professionals

Become an AI Powered Business Leader & Professional who is Equipped with knowledge about the Modern Machines

faq

Is this only for engineers?

Mixed cohort: engineers own implementation details while PMs/COE own metrics and labeling budgets.

← All curriculum samples·training hub