Context Engineering: Beyond Prompt Engineering

Course Prerequisite(s)

Please note that this course has the following prerequisites which must be completed before it can be accessed
Prompt Engineering Mastery: From Foundations to Future
Prompt Engineering Mastery II: Advanced Reasoning, Agents and Prompt Optimisation

About Course

Context Engineering: Beyond Prompt Engineering

A clear prompt is only part of a reliable AI workflow. The system also needs the right information, in the right form, at the right moment, with appropriate permissions and a way to verify what happened.

This advanced conceptual course from NORAI Academy teaches you to inspect those decisions. Through 70 text lessons across 14 sections, you will examine context selection, evidence, memory, tools, agent workflows, security and evaluation in plain English.

Understand the whole workflow

Follow Kajo, a fictional professional services assistant, through a series of independent, fully specified cases. Reconstruct what was available, what reached the model, what the system was permitted to do and what actually changed. Then apply the same principles to unfamiliar research and software operations cases.

Define task contracts, acceptance conditions and authority boundaries.
Preserve meaning through tables, visual evidence, extraction and summaries.
Select complete context under explicit resource limits.
Distinguish tool requests, uncertain outcomes and verified effects.
Review memory updates, compaction and continuity across sessions.
Compare workflows, delegation, handoffs and concurrent state changes.
Recognise prompt injection, poisoned information and unsafe cross boundary reuse.
Interpret experiments, references, reliability and evaluation leakage.
Assess cost, latency, release tradeoffs and recovery evidence.

Learn through worked decisions

Every section includes a worked dossier studio and a glossary. Eight reusable tools help you organise evidence, calculate context budgets, review memory changes, verify operations and prepare a design review. Private practice is unsubmitted and includes worked explanations.

Automatically marked assessments

Fourteen section quizzes and a separate final assessment contain 164 questions with fixed answer keys. You will select supported conclusions, interpret traces, compare packets and perform simple calculations. No essay submission, instructor marking or AI grading service is required.

The pass requirement is at least 80% in every assessment: 8 of 10 in each section and 20 of 24 in the final. Attempts are unlimited and there is no time limit.

Who this course is for

This course suits professionals, consultants, product owners and technical learners who already understand basic prompting and LLM concepts. Prompt Engineering Mastery or equivalent knowledge and a foundation in RAG are recommended. You do not need programming experience, a paid AI subscription or access to production infrastructure.

Research informed and carefully bounded

The October 2026 edition includes optional primary research and official technical readings. The lessons explain the assessed concepts, and all necessary case facts are supplied. Fictional policies, measurements and organisations are teaching inputs, not promises about commercial systems.

By the end, you should be able to review a context design, distinguish demonstrated failures from untested explanations and justify the next investigation or improvement. Passing fixed case assessments does not independently certify production implementation competence or provide university credit.

Reconstruct the information and control state available at a specified point in an AI workflow.
Evaluate candidate context packets against task, authority, coverage, privacy and resource constraints.
Distinguish proposed actions, tool observations and confirmed changes in the external environment.
Assess memory writes, updates, retrieval and compaction for scope, time and preserved meaning.
Compare bounded workflows and agent handoffs under explicit coordination requirements.
Locate trust boundary failures and select controls at the layer where enforcement is required.
Interpret evaluation evidence and propose discriminating tests without overstating causality or generality.
Assess a context design and release proposal against evidence, operating constraints and known limitations.

Course Content

From prompts to observable context
Reconstruct what a system receives and distinguish model input from application state and the outside world.

1.1 The model sees a packet, not your whole world
1.2 Instructions, evidence, state and capability
1.3 The context lifecycle
1.4 Read an observable trace
1.5 A baseline before architecture
Section 1 assessment: Reconstruct the invocation

Task contracts and authority
Define success, scope and justified uncertainty before selecting context or allowing actions.

Representations that preserve meaning
Build interpretable context packets with explicit provenance, units, relationships and uncertainty.

Context selection under constraints
Select useful information under explicit evidence, resource and uncertainty requirements.

Evidence acquisition beyond a document dump
Choose evidence acquisition paths according to the claim, source state and current task.

Tools, observations and consequential actions
Read tool contracts and distinguish intended operations from verified changes in the environment.

Memory as a governed change process
Control what persists, what changes and what may be recalled across sessions.

Long tasks, compaction and continuity
Preserve the state needed to resume work without promoting summaries into unsupported truth.

Workflow control and bounded autonomy
Allocate decisions between fixed control and agent choices while preserving dependencies and stopping conditions.

Coordination, handoffs and shared state
Assess when multiple agents help and how their information, responsibilities and updates can conflict.

Security across context boundaries
Locate how untrusted content can affect instructions, memory, tools and disclosure across a workflow.

Experiments, evaluation and causal diagnosis
Test context changes with matched cases, valid references and appropriately limited conclusions.

Operating tradeoffs and change assurance
Assess the cost, performance and maintenance implications of a context design under explicit release criteria.

The context design review
Integrate the course into an evidence based review and distinguish durable principles from promising research.

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Context Engineering: Beyond Prompt Engineering

Course Prerequisite(s)

About Course

Context Engineering: Beyond Prompt Engineering

Understand the whole workflow

Learn through worked decisions

Automatically marked assessments

Who this course is for

Research informed and carefully bounded

What Will You Learn?

Course Content

From prompts to observable context Reconstruct what a system receives and distinguish model input from application state and the outside world.

1.1 The model sees a packet, not your whole world

1.2 Instructions, evidence, state and capability

1.3 The context lifecycle

1.4 Read an observable trace

1.5 A baseline before architecture

Section 1 assessment: Reconstruct the invocation

Task contracts and authority Define success, scope and justified uncertainty before selecting context or allowing actions.

2.1 Turn a request into acceptance conditions

2.2 Authority is not confidence or recency

2.3 Identity, purpose and scope

2.4 Answerability and useful uncertainty

2.5 A context contract that can be tested

Section 2 assessment: Task contracts and authority

Representations that preserve meaning Build interpretable context packets with explicit provenance, units, relationships and uncertainty.

3.1 The anatomy of a context record

3.2 Tables, units and relationships

3.3 Schemas as interfaces, not truth machines

3.4 Visual and mixed evidence

3.5 Provenance without context clutter

Section 3 assessment: Representations that preserve meaning

Context selection under constraints Select useful information under explicit evidence, resource and uncertainty requirements.

4.1 Capacity is not demonstrated capability

4.2 Budget the complete invocation

4.3 Coverage as a constraint

4.4 Ordering, repetition and counterexamples

4.5 When to obtain more information

Section 4 assessment: Context selection under constraints

Evidence acquisition beyond a document dump Choose evidence acquisition paths according to the claim, source state and current task.

5.1 A concise bridge from RAG

5.2 Complete sets and exact calculations

5.3 Static references and changing environments

5.4 Progressive disclosure and discovery

5.5 Evidence acquisition plans

Section 5 assessment: Evidence acquisition beyond a document dump

Tools, observations and consequential actions Read tool contracts and distinguish intended operations from verified changes in the environment.

6.1 Tool descriptions are part of context

6.2 Observation, status and external truth

6.3 Timeouts, retries and duplicate effects

6.4 Constrain the action envelope

6.5 Verify useful completion

Section 6 assessment: Tools, observations and consequential actions

Memory as a governed change process Control what persists, what changes and what may be recalled across sessions.

7.1 Memory is more than a conversation archive

7.2 The write decision

7.3 Time, corrections and supersession

7.4 Retrieval and permission for remembered material

7.5 Evaluate memory across changes

Section 7 assessment: Memory as a governed change process

Long tasks, compaction and continuity Preserve the state needed to resume work without promoting summaries into unsupported truth.

8.1 What must survive a context reset

8.2 Compaction is a transformation

8.3 Checkpoints and invariants

8.4 Rehydration and changing reality

8.5 Continuity tests

Section 8 assessment: Long tasks, compaction and continuity

Workflow control and bounded autonomy Allocate decisions between fixed control and agent choices while preserving dependencies and stopping conditions.

9.1 The harness around the model

9.2 Decompose by dependencies

9.3 Fixed paths and runtime choices

9.4 Stopping, escalation and recovery

9.5 Compare complete trajectories

Section 9 assessment: Workflow control and bounded autonomy

Coordination, handoffs and shared state Assess when multiple agents help and how their information, responsibilities and updates can conflict.

10.1 A reason to split the work

10.2 The handoff contract

10.3 Shared memory and lost updates

10.4 Agreement, duplication and correlated error

10.5 Evaluate the team as a system

From prompts to observable context
Reconstruct what a system receives and distinguish model input from application state and the outside world.

Task contracts and authority
Define success, scope and justified uncertainty before selecting context or allowing actions.

Representations that preserve meaning
Build interpretable context packets with explicit provenance, units, relationships and uncertainty.

Context selection under constraints
Select useful information under explicit evidence, resource and uncertainty requirements.

Evidence acquisition beyond a document dump
Choose evidence acquisition paths according to the claim, source state and current task.

Tools, observations and consequential actions
Read tool contracts and distinguish intended operations from verified changes in the environment.

Memory as a governed change process
Control what persists, what changes and what may be recalled across sessions.

Long tasks, compaction and continuity
Preserve the state needed to resume work without promoting summaries into unsupported truth.

Workflow control and bounded autonomy
Allocate decisions between fixed control and agent choices while preserving dependencies and stopping conditions.

Coordination, handoffs and shared state
Assess when multiple agents help and how their information, responsibilities and updates can conflict.

Security across context boundaries
Locate how untrusted content can affect instructions, memory, tools and disclosure across a workflow.

Experiments, evaluation and causal diagnosis
Test context changes with matched cases, valid references and appropriately limited conclusions.

Operating tradeoffs and change assurance
Assess the cost, performance and maintenance implications of a context design under explicit release criteria.

The context design review
Integrate the course into an evidence based review and distinguish durable principles from promising research.