Building Documentation Fluency and Tools
I.
Practice Habits for Documentation
A.
Suggested Practice Activities
- 1.Practice writing one piece of documentation intended for someone you have never met, then ask an actual colleague unfamiliar with the topic to read it and identify anything confusing.
- 2.Build the habit of naming files and pages consistently, by date, topic, and status, so a colleague could find something without asking you directly.
- 3.Ask to observe how an existing piece of team documentation was originally structured, even though setting one up from scratch is not yet expected, so the underlying structure is familiar when that responsibility arrives.
- 4.Periodically audit your own older documentation and ask whether it is still accurate; documentation stewardship does not end when a document is first published.
II.
Tool Categories
| Tool Category | Representative Tools | How an OFA 1 Uses Them |
|---|---|---|
| Documentation platforms | SharePoint, Confluence | Finding and maintaining the single source of truth for a workstream |
| Version history and change tracking | Native version history in SharePoint, Confluence, and Word | Seeing what changed and when, without needing a separate system |
| Data dictionaries and reference tables | Shared spreadsheets, read-only database schema documentation | Understanding what a field or table actually represents before documenting a process that touches it |
| AI drafting and reorganization assistance | Approved AI writing assistants | Consolidating scattered notes into a first-draft structured document |
III.
Data Dictionaries in Documentation
- A.Good documentation about a data-driven process often needs to reference what data means, not just what a screen looks like.
- B.A data dictionary - a simple reference describing what each field or table in a system represents - is a common artifact a foundational OFA should learn to read and, over time, contribute to.
- C.For example, documenting a triage process that depends on an "ActivityStatus" field is far more useful if the documentation also notes, in plain language, what values that field can hold and what each one means, rather than assuming every future reader already knows.
A.
A simple data dictionary entry might look like this:
Field: ActivityStatus Table: Activities Allowed Values: 0 = Draft (not yet visible to members) 1 = Published (visible and open for RSVP) 2 = Closed (past the RSVP deadline) 3 = Cancelled
- Field·ActivityStatus Table: Activities Allowed Values: 0 = Draft (not yet visible to members) 1 = Published (visible and open for RSVP) 2 = Closed (past the RSVP deadline) 3 = Cancelled
An OFA 1 should get comfortable asking a technical teammate what a given field actually means and recording the answer.
B.
Looking Ahead to OFA 2
Once independently maintaining documentation for a workstream, an OFA proactively builds a small data reference section into that documentation wherever the workstream depends on specific fields, tables, or codes.
IV.
AI for Consolidating Notes
- A.AI tools are well suited to reorganizing scattered raw notes into a structured first draft - exactly the kind of consolidation exercise practiced in this module's Applied Practice and Evaluation activity.
- B.As with other modules, the output is a draft·contradictions between source notes must still be resolved by the OFA.
- C.An AI tool will often smooth over a contradiction rather than flag it, which is the opposite of what good documentation stewardship requires.