Skip to main content

Creating documentation for different audiences

Instruction iconUnit
Complete iconIn Progress

Imagine explaining your ML model the same way to your head of product and a junior data scientist. One gets lost in jargon, the other in vagueness. That’s why a one-size-fits-all approach to documentation doesn’t work.​

Clear documentation sets the foundation for ML projects, but it must be tailored to its audience, offering engineers technical depth and giving business leaders and compliance teams clear, actionable insights.

Different audiences banner

Writing for different audiences​

Two strategies can help you provide the right level of detail without overloading or oversimplifying:

1. Audience-specific language​

  • Technical audiences: Expect precise terminology, code snippets, architecture diagrams, and dependency lists.
  • Non-technical audiences: Look for clarity on what the model does, why it matters, and its limitations—without being buried in jargon.

2. Layered approach​

Documentation should have layers like an onion:

  • Layer 1: High-level summary (accessible to everyone).
  • Layer 2: Intermediate detail (focused on business use and metrics).
  • Layer 3: Deep technical detail (for reproducibility and troubleshooting).

Tailoring documentation in practice​

Technical Documentation​

  • Model architecture diagrams.
  • Data pipelines and preprocessing steps.
  • Training scripts and benchmarks.
  • Dependency lists (e.g., requirements.txt).

Non-technical Documentation​

  • Business value: Problem solved and impact.
  • Model limitations: Where human oversight is required.
  • Ethical considerations: Fairness and bias.
  • Intended use cases: Proper and improper applications.

Case study: Improving transparency in a hospital triage model​

Challenge: Initial docs were too technical, leading to physician mistrust and engineering confusion about integration.Solution: A Layered Approach

  • Layer 1 (Administrators): Focused on reducing wait times and resource optimization.
  • Layer 2 (Medical Staff): Focused on clinical usability, age-group accuracy, and known conditions with reduced performance.
  • Layer 3 (Engineers): Focused on training logs, pipelines, and deployment instructions.Impact: Physicians understood model outputs, administrators saw ROI, and engineers could maintain the system with confidence.

Action item: Pause and reflect — same model, different lens​

Imagine you've built a model to detect crop disease using satellite imagery. You need to document this for:

  1. Engineers (deployment/retraining).
  2. Agricultural experts (interpreting field predictions).
  3. Executives (business impact).
  • What key information would you include for each?
  • How would your language change depending on the audience?

Key info per group:

Adapting language: