> For the complete documentation index, see [llms.txt](https://help.transform.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.transform.ai/ai/knowledge-management.md).

# Knowledge Management

Keep institutional knowledge organized and easy to reuse.

### Overview

Knowledge management keeps delivery context reusable across Projects.

It reduces rework caused by repeated discovery.

### Key Concepts

* **Source of truth**: the record that should be referenced during delivery
* **Traceability**: links between meeting context, requirements, tasks, and tests
* **Retention**: how long information remains available (plan dependent)

### How It’s Used

Use Artifacts for reference material and outputs that need a stable home.

Use comments and history for decisions made in the flow of work.

Use Console for retrieval across Projects when the location is unknown.

### Benefits

* Faster onboarding for new delivery teams
* Cleaner audits for approvals and scope changes
* Less duplication across Projects

### Setup Steps

{% stepper %}
{% step %}

### Standardize capture locations

1. Store reference files as Artifacts.
2. Record decisions in comments using a consistent pattern.
   {% endstep %}

{% step %}

### Link sources instead of duplicating text

Link requirements, tasks, and meeting records to preserve origin context.
{% endstep %}

{% step %}

### Define ownership

Assign ownership for the core knowledge set, such as process definitions and templates.
{% endstep %}
{% endstepper %}

### Best Practices

* Prefer stable naming across Projects.
* Archive outdated content when auditability matters.
* Keep Segment Types stable to protect reporting continuity.

### Summary

Start here:

* [What Are Artifacts?](/artifacts/what-are-artifacts.md)
* [Console AI Search](/ai/console-ai-search.md)

Related:

* [Comments and History](/collaboration-and-automation/comments-and-history.md)
* [Audit Trail](/transformations/audit-trail.md)


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://help.transform.ai/ai/knowledge-management.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.