Module 5 — Context Engineering and Structural Prompting
What this module is about
You've built the architecture: the ontology, the agents, the workflows, the tools. But here's a question that's easy to overlook: how does each agent know what to do?
The answer is: through its context — the complete package of information, instructions, and rules that shapes the agent's behavior. In traditional software, this is the code. In AI systems, this is the prompt and its surrounding context.
Context engineering is the discipline of designing what an agent sees, knows, and is told to do — as carefully as you'd design any other system component. And structural prompting is the technique of organizing instructions so that agent behavior is predictable, consistent, and controllable.
If the previous modules built the highway, the exits, and the vehicles — this module teaches the drivers how to navigate, where to look, and how to stay in their lane.
Learning outcomes
By the end of this module, you will be able to:
- Design context packages that separate static rules from dynamic information from retrieved knowledge
- Build layered instruction systems with clear hierarchy: identity, policy, task, output
- Apply structural prompting techniques to reduce hallucination, enforce output schemas, and handle uncertainty
- Measure and improve prompt consistency through systematic testing and validation
Module map
| # | Content | Type | Estimated time |
|---|---|---|---|
| 1 | Context architecture — what the agent sees | Lesson | 90 min |
| 2 | Prompt contract design — how the agent is told to behave | Lesson | 90 min |
| 3 | Validation and consistency — proving it works | Lesson | 90 min |
| A | Context package v1 | Application | 3 hours |
| B | Prompt hardening sprint | Application | 2 hours |
| — | Assessment | Evaluation | 30 min |
What you will build
- A context map decomposing a monolithic prompt into modular, layered components
- A structural prompt template with identity, policy, task, schema, and refusal sections
- A consistency report measuring agent behavior before and after structural improvements
- A versioned context package with mission, policies, ontology references, output schema, and change log
- A prompt hardening case study with before/after metrics on cost, latency, and quality
Prerequisites
- Module 4 completed (you need workflows and tool contracts to design context around)
- Familiarity with how language models process prompts (no deep ML knowledge required)
Estimated time
10-12 hours total (lessons + applications + assessment)