dooopSoftware · Organization · 5 min
How to Create a Development Example Library
Organize AI development examples by task, context, and decision. Define curation, validity, and search before expanding the library.
Published on September 6, 2026
CENTRAL THESIS
A loose prompt does not explain when the practice applies or why it was accepted.
The library must make decisions reusable without hiding their conditions and limits.
An internal library of AI development examples should help someone recognize a task, understand a decision, and assess whether a practice fits their context. A collection of loose prompts does not provide this foundation. Define a minimum record for each example, organize search by work need, and maintain a person responsible for reviewing or removing materials that have lost validity.
Choose What Deserves to Enter the Library
Start with examples that answer recurring team questions. This could be a well-defined task, a rejected suggestion with a clear reason, a test that revealed a flaw, or a useful way to record context.
Do not select only well-presented results. A case that shows a limit can help more than an unexplained demonstration. The material needs to state what was observed and what cannot yet be concluded.
The DORA on documentation highlights clarity, ease of location, and reliability. Use these attributes as curation criteria: can the example be understood, found, and used without relying on the person who produced it?
Create a Record with Context and Decision
A short record can include:
- Task and problem that motivated the use of AI.
- Necessary context and relevant constraints.
- Input or request used, when it can be shared.
- Examined output and decision to accept or reject.
- Evidence of verification and case limits.
- Conditions to adapt the practice.
- Responsible person, version, and reason for future review.
The prompt is a possible component, not the mandatory center of the record. In some cases, the main contribution will be in the choice of context or the criterion used to reject the response.
Prepare materials according to the organization's access agreements. Remove data that does not need to circulate and preserve a sufficient description of the task. If removing context prevents understanding, the material may need another format or more restricted access.
Organize Search by Work Question
Tool categories can help but tend to answer only "what was done with." Add ways to find the example by task, type of decision, and difficulty.
Someone looking for how to review a refactoring needs to locate criteria for preserving behavior, even if using another assistant. Someone investigating outdated documentation needs to find the case by the problem, not remember which model was used.
Test the organization with someone who did not participate in curation. Ask them to find an example for a concrete task and explain when they would use it. Observation may reveal unclear names, lack of context, or duplicate materials.
Fictional Example: A Library of Useful Reviews
Imagine a team starting to gather cases of AI-assisted change reviews. One example shows a refactoring proposal rejected because it altered a business exception. The record presents the original intention, the relevant difference, and the test used to check behavior.
Another person finds the case while preparing a similar review. They do not copy the code. They use the example's questions to look for exceptions in their own task and record where the context differs.
The expected value lies in this guided adaptation. The example does not demonstrate that the library will reduce defects or review time. The team will need to observe whether materials are found and if they help real decisions.
Maintain Understandable Validity States
Differentiate material in development, examined reference, outdated example, and content withdrawn from use. The state needs to be visible before someone applies the practice.
Define review events: tool change, business rule change, environment swap, or report that the example no longer works as described. It is not necessary to review everything with the same frequency, but there must be a person responsible for responding to signals.
The DORA on learning culture proposes treating learning as an organizational investment. Maintaining the library is part of this investment. If no one has time for curation, the accumulated volume should not be confused with available capacity.
Evaluate Use Before Expanding the Collection
Track questions such as: was the material found? Did the reader understand its limits? Did adaptation generate a new question? Was the example used as a reference or copied without checking context?
The Microsoft ExP describes hypotheses, measurement, and iteration in development. For the library, propose a utility hypothesis and observe a usage sample. The number of files and accesses alone does not demonstrate practice improvement.
Start with few examples the team really needs to consult and test their understanding. Expand the collection when there are entry, search, and maintenance criteria capable of preserving the materials' usefulness. The library should store reusable decisions with their limits, not just traces of conversations with tools.
If you want to discuss this decision in your company's context, talk to dooop.
Further Reading
- How to Prepare a Software Company to Work with AI
- How to Teach Critical Review of AI Outputs
- How to Train Developers to Work with AI
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