How to give AI the context needed to build a complete product
Replace the oversized master prompt with a layered context system covering product facts, architecture, specialist knowledge, current work and verification evidence.
Give AI context in layers: a stable product brief, explicit architecture and constraints, specialist knowledge selected for the task, a bounded current objective and evidence from the real implementation. Keep decisions versioned and remove outdated instructions.
Layer 1: product truth
Start with the durable facts: who the product serves, what outcome it creates, what is in scope, what is explicitly out of scope and which constraints cannot be violated.
This should be short enough to remain current. If the brief becomes a history of every conversation, it stops acting as a source of truth.
- Audience and core outcome.
- Actors and business-critical flows.
- Non-goals, legal constraints and supported environments.
Layer 2: architecture and decisions
Describe components, data ownership, access boundaries, integrations and deployment. Store important decisions with their reason and consequences.
The model should know which existing decisions it must preserve and which questions are still open.
Layer 3: relevant specialist depth
Do not load every possible fact into every task. Route the current work to the relevant specialist knowledge: authentication for identity flows, RLS for tenant data, billing for entitlements, privacy for retention and deletion.
Connected specialists matter because one change often affects several layers. Adding social login can change account linking, recovery, privacy disclosure and support behavior.
Layer 4: bounded work and verification
The current request should define one outcome, acceptance criteria and the artifacts available for inspection. After implementation, add build results, tests, browser checks and remaining limitations.
This closes the loop. Context is not only what the AI was told before the task; it also includes what the real system proved afterwards.
- One bounded outcome per task.
- Explicit affected layers and assumptions.
- Evidence before completion is declared.
Common questions
Is this just prompt engineering?
No. Prompt engineering shapes one request. A context system maintains durable product truth, specialist knowledge, decisions and verification across many requests and tools.
Should every file be loaded into every request?
No. Keep stable sources available, then select the files relevant to the current decision so critical constraints are not buried in noise.
How does Advea fit?
Advea supplies the portable specialist layer and an operating method for noticing implicit requirements and verifying the result.
Primary references
Platform behavior changes. These primary sources define the external capabilities referenced in this guide.
OpenAI: Projects in ChatGPTCursor: Rules