Skip to content

ai-context-layering.md

Latest commit

 

History

History
81 lines (57 loc) · 2.58 KB

ai-context-layering.md

File metadata and controls

81 lines (57 loc) · 2.58 KB
Raw

AI Context Layering

AI generation should be grounded in layered context.

The goal is to avoid generic outputs and avoid copying references.

Layers

Use a stable context order:

Layer Content Purpose
1. Product rules Immutable product policy, output constraints, anti-copy stance Keep generation aligned
2. Channel profile Niche, audience, voice, language, goals, boundaries Adapt output to the creator
3. Target video or idea Current title, description, topic, transcript if allowed, known metrics Ground the task
4. References and patterns Saved references, analyses, synthesized patterns, measured signal Use niche memory
5. Objective and surface Improve existing video, prepare publication, generate idea, search/browse/suggested intent Focus the output
6. Output schema Required fields and validation shape Make output machine-checkable

Prompt Assembly

[Product rules]
[Channel profile]
[Target video or idea]
[References and synthesized patterns]
[Objective and surface]
[Output schema]
[Final instruction]

The order matters. The task-specific objective should arrive after the general context so the model can apply the right context to the current request.

Required Channel Profile

For high-value generation, channel profile should be required.

If channel context is missing, a system should prefer a clear error over a generic generation. Generic generation weakens the product and makes outputs harder to trust.

Output Shape

A publication plan should not be only a list of titles.

Useful fields:

  • Title variants.
  • Thumbnail briefings.
  • Recommended title and thumbnail pairings.
  • Description guidance.
  • Tags or search terms.
  • Angle.
  • Why it might work.
  • Risk.
  • What not to copy from references.
  • Confidence or evidence notes.

Secondary Coherence Pass

Title and thumbnail should tell one story.

A useful pattern is a second pass that evaluates:

  • Do the title and thumbnail create the same promise?
  • Is there a mismatch between curiosity and visual direction?
  • Which pairs are strongest?
  • What risk should the creator watch?

This pass can return a packaging coherence score and recommended pairs.

Validation

Validate structured outputs before saving them:

  • Required fields exist.
  • Arrays have expected lengths.
  • Unsupported enum values are rejected.
  • Anti-copy fields are present.
  • Promises do not claim guaranteed growth.
  • The output can be rendered by the UI without custom parsing.

Invalid AI output should fail the job or trigger regeneration, not leak into the user experience.