Every engineer on your team now drafts specifications, designs, and papers with an AI model. The format, evidence, and design vocabulary of that output determine how fast you can review it and how safely you can ship it. Claude’s organization instructions let an Owner on a Team or Enterprise plan set one standard that Claude follows in every conversation across the organization [1]. I use that field to set the output standard for EverBetter.
The standard has seven clauses. Every AI response produced inside our organization follows them.
- A TL;DR at the very top.
- An executive summary immediately after.
- Citations for every claim, in IEEE bracketed numerals.
- The word “Inference” on any statement the model cannot prove.
- UML for designs; IEEE format for white papers and technical papers.
- Deference to established software design patterns [3].
- One closing tip on using AI more efficiently.
The header clauses reduce review time. A fixed TL;DR and executive summary let a reviewer read two lines and decide whether the rest of the document needs attention.
The evidence clauses protect patients. EverBetter builds EHR and PHR software, and an uncited claim in a clinical or compliance document is a liability. The Inference label separates evidence from extrapolation, the same way a radiologist separates findings from impression. A reviewer sees the label and knows which sentences require verification.
The design clauses give the team one vocabulary. UML [4] and the Gang of Four catalog [3] define each diagram and each pattern precisely. Reviewers compare a design against the pattern catalog instead of against personal preference. IEEE style [5] does the same for technical prose.
The closing tip trains the team. Each deliverable teaches its author one technique for using AI more efficiently, and the practice spreads without a training session.
The precedence rule applies to me as well. My personal preferences [2] request a BLUF. Our organization instructions forbid BLUF and require an “EX” summary instead. The organization layer wins [1], so my own sessions follow the company format. I now write the organization layer for invariants and keep the personal layer for individual taste.
The organization field has a 3,000-character limit [1]. The limit requires policy written like a specification: one requirement per sentence and no adjectives.
A system prompt sets policy; it does not enforce it. Pair it with a deterministic check: a short CI script that rejects any specification missing a TL;DR, an executive summary, or a References section. The script consumes zero LLM tokens and produces the same result on every run.
AI tip: Before you publish organization instructions, paste them beside a teammate’s personal preferences and ask Claude to list every contradiction. Resolve each conflict before the instructions take effect.
References
[1] Anthropic, “Set organization instructions,” Claude Help Center. [Online]. Available: https://support.claude.com/en/articles/14546867-set-organization-instructions. Accessed: Oct. 13, 2026.
[2] Anthropic, “Understanding Claude’s personalization features,” Claude Help Center. [Online]. Available: https://support.anthropic.com/en/articles/10185728-understanding-claude-s-personalization-features. Accessed: Oct. 13, 2026.
[3] E. Gamma, R. Helm, R. Johnson, and J. Vlissides, Design Patterns: Elements of Reusable Object-Oriented Software. Reading, MA, USA: Addison-Wesley, 1994.
[4] Object Management Group, OMG Unified Modeling Language (OMG UML), Version 2.5.1, formal/17-12-05, Dec. 2017.
[5] IEEE, IEEE Editorial Style Manual for Authors. Piscataway, NJ, USA: IEEE. [Online]. Available: https://journals.ieeeauthorcenter.ieee.org/