Case study

Building a RAG content readiness framework at scale

As teams began adopting GenAI and retrieval-based systems, a new question emerged: is our content actually usable by AI? I helped organize and structure a cross-functional initiative to define RAG readiness and turn it into practical, usable guidance.

Focus
Content systems, RAG, AI readiness
Scale
20–30 contributors across workstreams
Approach
Structured working group + framework design
Outcome
Shared model for RAG-ready content

The problem

There was no shared definition of RAG-ready content. Teams were experimenting with AI, but content created for humans did not reliably work for retrieval, interpretation, or reuse by AI systems.

  • No shared definition of RAG readiness
  • Content failed under retrieval
  • No evaluation model
  • Discussions were not actionable

Building the initiative

I helped organize a working group of 20–30 contributors and structured the effort into focused workstreams. This created a system for collaboration that turned a broad problem into concrete outputs.

Workstreams

Structure, metadata, clarity, style, metrics, rollout

Coordination

Aligned contributors, defined scope, structured collaboration

Outcome

Moved from discussion to actionable guidance

The framework

Structure

Topics as independent units, clear hierarchy, scannable formatting

Metadata

Descriptive titles, applicability, context disambiguation

Clarity & Style

Precise terminology, defined concepts, clear sentence structure

What this revealed

Retrieval is not just a search problem. It is a design problem. Content must stand alone, carry its own context, and be interpretable when retrieved out of sequence.

RAG readiness is not a feature. It is a property of how content is structured, written, and contextualized.

What this enabled