Context Engineering

The Five Layers of Context
Your AI Is Missing

Most teams operate with half of one layer. Here is the full architecture.

The Five Context Layers — from foundation to execution
Layer
01
Foundation Instructions

The system-level directive — role, goal, constraints, and non-negotiables the model must operate within. The layer most teams skip or shortchange entirely.

Layer
02
High leverage Examples

Few-shot demonstrations of what good output looks like. A model that has seen two strong campaign briefs will produce better briefs than one that has seen none.

Layer
03
Task-specific Situational Data

The facts specific to this task — audience segment, product details, market context, competitive positioning. Information the model cannot infer and must be given.

Layer
04
Often absent Memory

Prior interactions, decisions, feedback, or outputs relevant to the current task. In most production workflows this layer is absent entirely — forcing the model to start cold every session.

Layer
05
Advanced Tool Access

External resources the model can query or act on — search, databases, APIs. Relevant when the task requires information that cannot fit in the context window directly.

The production gap: Most teams that struggle with AI output quality are operating with only half of one of these layers — a single-line prompt with no other context whatsoever. The model isn't the problem. The information environment is.
The same model. Two different information environments. Dramatically different results.
Without context engineering
Prompt sent:
"Write a campaign brief for our Q2 product launch."
AI output:
Campaign Brief — Q2 Product Launch. Objective: Increase awareness and drive sales. Target audience: Potential customers interested in the product category. Key messages: Innovative, high-quality, competitively priced...
Generic. Could apply to any product at any company. Requires a full rewrite before it can be used.
With context engineering
Context template + prompt:
BRAND: Vanta Analytics — B2B SaaS, mid-market. PRODUCT: DataPulse v3. POSITIONING: "First BI tool built for revenue ops." PERSONA: RevOps Manager, 200–800-person SaaS. TONE: Confident, data-grounded. TASK: Q2 brief targeting RevOps leaders. GOAL: 400 MQL in 90 days via LinkedIn + email.
AI output:
Campaign: "Revenue Ops Runs on Pulse" — Core message: Your revenue data is 72 hours old. DataPulse shows you what's happening now — live in 90 minutes. Channels: LinkedIn targeting RevOps titles at 200–800 SaaS companies...
Actionable. Specific. Can be reviewed and approved as-is.