AI curriculum

Course outline for Context Engineering and Context Graphs

A fresh agent session can lose a decision's history or act on stale evidence. Build a task-specific context package from synthetic logs, calendar records, and messages using source, time, and access rules. Preserve historical decisions separately from current knowledge.

About Context Engineering and Context Graphs

A fresh agent session can lose a decision's history or act on stale evidence. Build a task-specific context package from synthetic logs, calendar records, and messages using source, time, and access rules. Preserve historical decisions separately from current knowledge.

Context Engineering and Context Graphs Course Objectives

  • Distinguish stable knowledge, activity history, execution dependencies, and task context.
  • Model source, time, user, and access constraints for context facts.
  • Define required facts, freshness limits, and source precedence for one stage.
  • Retrieve a bounded package while excluding irrelevant or prohibited data.
  • Reconstruct the task in a fresh session and report unresolved conflicts.

Pre-requisites

  • Understanding of agent inputs and explicit knowledge models.
  • Graph familiarity, including entities, relationships, and edge meanings.
  • Basic data modeling or application design skills; a full graph database implementation is not required.

Before this course

Complete the required courses or bring equivalent experience. Recommended courses add useful depth; they are not enrolment gates.

Required preparation

Recommended preparation

Readiness checks

  • Graph-model familiarity: entities, relationships, and edge meanings
  • Basic data modeling or application design

Understands agent inputs and explicit facts before selecting contextual evidence; check graph literacy directly without requiring a full graph-database implementation course.

Find your place in the learning map

Lab Setup

  • Graph editor or local graph library and a text editor or scripting runtime.
  • Synthetic logs, calendar records, user profiles, access labels, and conflicting/stale evidence fixtures.
  • Supplied context budget and task contract; a live model is optional and no personal records are needed.

Detailed Course Outline

Module 1: Model the situation, not every fact

  • Context vs knowledge: Separate stable domain facts from task-specific evidence, current workflow state, and historical decisions needed for one review.
  • Context graphs: Connect task-relevant facts with source, time, and access constraints without treating every available relationship as necessary context.
  • Activity graphs: Represent historical actions and decisions separately from stable knowledge and execution dependencies, preserving what happened rather than rewriting history.

Practical outcome: Separate stable facts, current workflow state, and historical activity in a synthetic project-review graph.

Module 2: Qualify sources and temporal meaning

  • Provenance: Attach source identifiers and versions to included facts so each context item can be checked against its evidence.
  • Temporal context: Distinguish historical decision snapshots from current information, applying freshness limits while flagging stale evidence and conflicting sources.
  • User context: Select role-relevant information under supplied access rules and exclude prohibited personal fields from the task package.

Practical outcome: Attach source/version/time/access fields and define freshness, conflicting-source, and prohibited-data rules.

Module 3: Assemble and recover stage context

  • Workflow context: Specify the current stage, required facts, and next permitted step instead of assembling an unrestricted project history.
  • Context retrieval: Retrieve bounded fresh evidence under source-precedence, relevance, access, and package-size rules, exposing unresolved conflicts rather than hiding them.
  • Context construction for agents: Build a stage-scoped manifest that supports fresh-session reconstruction while retaining source versions and the separate historical decision view.

Practical outcome: Build a stage-scoped context manifest and reconstruct the decision in a fresh session without replacing its historical snapshot.

Practical exercise

  • Produce a context graph and manifest for a synthetic daily review using logs, calendar entries, and messages.
  • Include required fresh evidence; exclude an irrelevant record and a prohibited field, and flag a stale or conflicting source.
  • Check every included fact against its source identifier and version, and enforce the supplied package-size budget.
  • Start with an empty session or worksheet and reconstruct the next permitted step solely from the manifest; compare historical and current decision views.

What you will produce

Assemble and recover a stage-scoped context package with freshness, source, and access checks.

How the work is checked

  • Context, knowledge, activity, and execution edges have distinct meanings.
  • Required facts are traceable and prohibited or irrelevant data is excluded.
  • Stale and conflicting evidence follows the stated precedence rules.
  • Fresh-session reconstruction preserves the historical snapshot and identifies the next permitted step.

Six-hour context-manifest lab; no full temporal graph service, guaranteed session memory, or automatic data-minimization claim.

When another approach is better

  • A versioned handoff document may suffice when there are few sources and no relationship queries.
  • Do not treat a smaller prompt as sufficient privacy protection or overwrite incident-time context with current facts.

How we train

Contact us for full course details, including duration, delivery options and lab requirements.

Ask about exercises, instructor feedback and the prior knowledge you need. Tool-specific courses marked provisional may become modules in a broader course.

Discuss your learning goals