docs: Updated glossary (key concepts) & FAQ

README.md

@@ -174,6 +174,19 @@ The script reads from:
 
 Output: Content is injected directly into `site/guide/templates/customize-document-templates.qmd` between marker comments.
 
+#### Chatbot product map and LLM corpus
+
+The in-app assistant (Valerie) uses generated files under `site/llm/`, including `chatbot-product-map.md` (platform routes mapped to docs URLs and section headings). CI regenerates that map and fails if it is out of date with your changes.
+
+If you edit `.qmd` files that affect linked docs or headings (for example FAQ or guide pages referenced from the product UI), regenerate and commit the map before opening or updating a pull request:
+
+```bash
+cd site
+make generate-chatbot-product-map
+```
+
+If product routes or in-app help links changed, use `make refresh-chatbot-product-map` instead (requires a local `validmind/frontend` checkout). See [`site/llm/README.md`](site/llm/README.md) for the full LLM render pipeline, snapshot maintenance, and when to refresh each artifact.
+
 #### Stylesheet organization (IN PROGRESS)
 
 The site uses a modular stylesheet architecture to maintain organized and maintainable styles:

site/_quarto.yml

@@ -103,7 +103,7 @@ website:
         - text: "Library and platform"
           file: about/library-and-platform.qmd
           contents:
-          - about/overview-model-documentation.qmd
+          - about/overview-documentation.qmd
           - about/overview-llm-features.qmd
           - text: "Deployment options"
             file: about/deployment/deployment-options.qmd

site/about/contributing/style-guide/conventions.qmd

@@ -125,7 +125,7 @@ Column 2, 50% wide
 
 Sometimes, it's helpful to highlight a call to action with a button that takes you to a topic or to a notebook on JupyterHub. 
 
-Change any Markdown link into a our theme-styled button by appending `{.button}`:
+Change any Markdown link into one of our theme-styled buttons by appending `{.button}`:
 
 :::: {.flex .flex-wrap .justify-around}
 
@@ -167,8 +167,8 @@ Using a markdown button also enables you to link to to the `.qmd` path instead o
     ```markdown
     <!-- IN THE BODY OF YOUR CONTENT -->
     - The record is registered in the inventory.[^1]
-    - You've already customized your model lifecycle statuses for use in workflows.[^2]
-    - Workflows have already been set up for use with your models.[^3]
+    - You've already customized your record stages for use in workflows.[^2]
+    - Workflows have already been set up for use with your records.[^3]
     - You are assigned a role that has access to complete actions set up by workflows.[^5]
 
     <!-- AT THE END OF YOUR .QMD PAGE -->
@@ -344,7 +344,7 @@ Use backticks to enclose keyboard commands, parameters, field values, and file n
 
 | Correct | Incorrect | 
 |------|-----|
-|   Learn how to store model identifier credentials in a `.env` file instead of using inline credentials.  |  Learn how to store model identifier credentials in a ".env" file instead of using inline credentials.  |
+|   Learn how to store record identifier credentials in a `.env` file instead of using inline credentials.  |  Learn how to store record identifier credentials in a ".env" file instead of using inline credentials.  |
 |   For example, the `classifier_full_suite` test suite runs tests from the `tabular_dataset` and `classifier` test suites to fully document the data and model sections for binary classification model use cases. |  For example, the "classifier_full_suite" test suite runs tests from the "tabular_dataset" and "classifier" test suites to fully document the data and model sections for binary classification model use cases.  |
 | Under When these conditions are met, you are able to set both `AND` and `OR` conditions. | Under When these conditions are met, you are able to set both "AND" and "OR" conditions.|
 : **Backtick** examples {.hover}
@@ -380,7 +380,7 @@ Within our documentation (`https://docs.validmind.ai/`), you are able to referen
 
 | Product Name | Variable Key | Description |
 |---:|---|---|
-| {{< var validmind.product >}} | `{{{< var validmind.product >}}}` | Comphrensive suite of tools with a {{< var vm.developer >}} for documenting and testing models, alongside a {{< var vm.platform >}} hosting cloud-based tools, APIs, databases, and validation engines. |
+| {{< var validmind.product >}} | `{{{< var validmind.product >}}}` | Comprehensive suite of tools with a {{< var vm.developer >}} for documenting and testing records (such as models), alongside a {{< var vm.platform >}} hosting cloud-based tools, APIs, databases, and validation engines. |
 | {{< var validmind.developer >}} |  `{{{< var validmind.developer >}}}` | Open-source library that connects to the  {{< var validmind.platform >}}. |
 | {{< var validmind.platform >}} |  `{{{< var validmind.platform >}}}` | Hosted multi-tenant architecture that includes a cloud-based web interface. |
 | {{< var validmind.api >}} | `{{{< var validmind.api >}}}` | Used to make calls to the {{< var validmind.developer >}}.[^21] |
@@ -438,7 +438,7 @@ From  **{{< fa gear >}} Settings** in the {{< var validmind.platform >}}, <br>yo
 - Set up your organization
 - Onboard new users
 - Manage roles, groups and <br>permissions
-- Configure the model inventory
+- Configure the inventory
 - Manage templates and workflows
 - And much more!
 

site/about/contributing/style-guide/voice-and-tone.qmd

@@ -47,7 +47,7 @@ Behind every page, there’s a person. In every word, lies an opportunity to win
 | Correct | Incorrect | 
 |------|-----|
 |   **User acknowledgement:** Documenting artifacts can be difficult and tedious for even the most seasoned of validators.  |  **User dismissal:** For experienced validators, documenting artifacts is a breeze.  |
-| **Success toast:** Nice work — you’ve successfully registered your first model! | **Inappropriate humor:** We lost your model documentation, oops! Here, have a pony! (e.g. error message for serious issue) |
+| **Success toast:** Nice work — you’ve successfully registered your first record! | **Inappropriate humor:** We lost your documentation, oops! Here, have a pony! (e.g. error message for serious issue) |
 : **Empathy & humor** examples {.hover}
 
 ### Be positive
@@ -82,7 +82,7 @@ Address the reader directly by using the second person.
 
 | Correct | Incorrect | 
 |------|-----|
-|   After completing this quickstart, you will be able to view your test results as part of your model documentation right in the {{< var validmind.platform >}}.  |  After completing this quickstart, the model developer will be able to view the test results as part of the model documentation right in the {{< var validmind.platform >}}.   |
+|   After completing this quickstart, you will be able to view your test results as part of your documentation right in the {{< var validmind.platform >}}.  |  After completing this quickstart, the developer will be able to view the test results as part of the documentation right in the {{< var validmind.platform >}}.   |
 : **2nd person** examples {.hover}
 
 ### Avoid stiff formality
@@ -92,7 +92,7 @@ Address the reader directly by using the second person.
 
 | Correct | Incorrect | 
 |------|-----|
-|   Once you’ve registered the model, you can then grab the unique code snippet that will have been generated for you to use in the next step.  |  First, you must register the model as this will generate a unique code snippet that needs to be copied. Then, you need to retrieve the code snippet so that you can make use of it in the following step.  |
+|   Once you’ve registered the record, you can then grab the unique code snippet that will have been generated for you to use in the next step.  |  First, you must register the record as this will generate a unique code snippet that needs to be copied. Then, you need to retrieve the code snippet so that you can make use of it in the following step.  |
 : **Informal language** examples {.hover}
 
 ### Focus on teamwork

site/about/contributing/validmind-community.qmd

@@ -11,7 +11,7 @@ aliases:
   - /about/join-community.html
 ---
 
-Work with financial models, in model risk management (MRM), or are simply enthusiastic about artificial intelligence (AI) and machine learning and how these tools are actively shaping our futures within the finance industry and beyond? Congratulations — you're already part of the {{< var vm.product >}} community! Come learn and play with us.
+Work with financial models, in model risk management (MRM), in AI governance, or are simply enthusiastic about artificial intelligence (AI) and machine learning and how these tools are actively shaping our futures within the finance industry and beyond? Congratulations — you're already part of the {{< var vm.product >}} community! Come learn and play with us.
 
 ::: {.callout}
 

site/about/deployment/deployment-options.qmd

@@ -26,23 +26,23 @@ Choose the {{< var vm.product >}} deployment option that best suits your organiz
 
 ![{{< var vm.product >}} architecture overview](validmind-architecture-overview.png){fig-alt="An image showing the ValidMind architecture"}
 
-In your own environment, model developers can continue to run models using your existing tools for data science and model development, such as Python, Jupyter Notebooks, and R, accessing data from sources such as Google Cloud Storage, Amazon S3, and Snowflake.
+In your own environment, developers can continue to run records (such as models) using your existing tools for data science and development, such as Python, Jupyter Notebooks, and R, accessing data from sources such as Google Cloud Storage, Amazon S3, and Snowflake.
 
-These models are then integrated with the {{< var validmind.developer >}}, which communicates with the {{< var validmind.platform >}} via our {{< var validmind.api >}}.
+These records are then integrated with the {{< var validmind.developer >}}, which communicates with the {{< var validmind.platform >}} via our {{< var validmind.api >}}.
 
 The {{< var validmind.platform >}} provides:
 
-- **Model inventory** — Centralized tracking and organization of models, accessible by developers, validators, and executives.
+- **Inventory** — Centralized tracking and organization of records, accessible by developers, validators, and executives.
 
 - **Documentation & validation engine** — Automated testing and documentation, with validation processes, ensuring compliance with regulations and internal policies.
 
 - **Template management** — Allows for easy creation, customization, and reuse of document templates.
 
-- **{{< var vm.product >}} dashboard** — A user-friendly interface providing insights, status updates, and governance reporting for model risk.
+- **{{< var vm.product >}} dashboard** — A user-friendly interface providing insights, status updates, and governance reporting for risk.
 
 ## Security & data privacy
 
-We ensure data security through strong data isolation, encryption, and role-based access controls.[^1] Personal identifiable information and customer data are not stored in model documentation. For more information, see our data privacy policy.[^2]
+We ensure data security through strong data isolation, encryption, and role-based access controls.[^1] Personal identifiable information and customer data are not stored in documentation. For more information, see our data privacy policy.[^2]
 
 ## Secure access
 

site/about/deployment/system-access-requirements.qmd

@@ -10,7 +10,7 @@ Allow list the following domains in your organization’s firewall to ensure you
 
 ## ValidMind Library Python API access
 
-To use our documentation automation tools and test suites for model developers and validators:
+To use our documentation automation tools and test suites for developers and validators:
 
 ```html
 *.validmind.ai

site/about/fine-print/data-privacy-policy.qmd

@@ -38,16 +38,16 @@ Understanding our policies shouldn’t feel like deciphering code, so we’ve ma
 
 The key points of our data privacy policy include:
 
-- **No personal identifiable information in documentation** — When the {{< var validmind.developer >}} generates documentation, it ensures that no personally identifiable information (PII) is included. This practice is a critical part of our commitment to protecting your privacy and maintaining the confidentiality of your data.
+- **No personally identifiable information in documentation** — When the {{< var validmind.developer >}} generates documentation, it ensures that no personally identifiable information (PII) is included. This practice is a critical part of our commitment to protecting your privacy and maintaining the confidentiality of your data.
 
-- **No storage of customer data** — {{< var vm.product >}} does not retain any customer datasets or models. This policy is in place in order to protect your data privacy and security. By not storing this information, {{< var vm.product >}} minimizes the risk of unauthorized access or data breaches.
+- **No storage of customer data** — {{< var vm.product >}} does not retain any customer datasets or records (models). This policy is in place in order to protect your data privacy and security. By not storing this information, {{< var vm.product >}} minimizes the risk of unauthorized access or data breaches.
 
 We believe it is important for users of {{< var vm.product >}}'s products to understand these practices as they reflect our dedication to data security and privacy.
 
 ::: {.callout-important}
 ## {{< var vm.product >}} does NOT: 
-- Include any personal identifiable information (PII) when generating documentation reports.
-- Store any customer datasets or models. 
+- Include any personally identifiable information (PII) when generating documentation reports.
+- Store any customer datasets or records (models). 
 :::
 
 ## Do you comply with the SOC 2 security standard?
@@ -64,13 +64,13 @@ The {{< var validmind.vpv >}} option provides all our features and services but
 
 Access is available through AWS PrivateLink, Azure Private Link, or GCP Private Service Connect, all of which provide private connectivity between {{< var vm.product >}} and your on-premises network without exposing your traffic to the public internet. 
 
-## What model assets are imported into documentation?
+## What record (model) assets are imported into documentation?
 
 When you generate documentation or run tests, {{< var vm.product >}} imports the following assets into the documentation via our {{< var validmind.api >}} endpoint integration:
 
 ![Artifacts imported into the documentation via our {{< var vm.api >}}](overview-api-integration.jpg){width=80% fig-alt="A representation of assets imported into the documentation via our Python API"}
 
-- Metadata about datasets and models, used to look up programmatic documentation content, such as the stored definition for _common logistic regression limitations_ when a logistic regression model has been passed to the {{< var vm.product >}} test suite to be run.
+- Metadata about datasets and records, used to look up programmatic documentation content, such as the stored definition for _common logistic regression limitations_ when a logistic regression model has been passed to the {{< var vm.product >}} test suite to be run.
 - Quality and performance metrics collected from datasets and models.
 - Output from tests and test suites that have been run.
 - Images, plots, visuals that were generated as part of extracting metrics and running tests.

site/about/glossary/_ai-governance.qmd

@@ -0,0 +1,42 @@
+<!-- Copyright © 2023-2026 ValidMind Inc. All rights reserved.
+Refer to the LICENSE file in the root of this repository for details.
+SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial -->
+
+AI ethics
+: A set of principles and practices guiding the responsible design, development, and deployment of AI systems. Common tenets include fairness, transparency, accountability, privacy, and human well-being.
+
+AI lifecycle
+: The end-to-end stages an AI system progresses through, including problem framing, data collection, model development, validation, deployment, monitoring, and retirement. Each stage carries distinct governance requirements.
+
+AI risk
+: The potential for adverse outcomes — financial, reputational, ethical, regulatory, or societal — arising from the design, deployment, or use of AI systems. AI risk extends beyond traditional model risk to include concerns such as bias, opacity, misuse, and unintended consequences.
+
+algorithmic accountability
+: The principle that organizations must take responsibility for the outcomes of the AI systems they deploy, including documenting decisions, monitoring performance, and providing mechanisms to identify and remediate harm.
+
+bias, algorithmic bias
+: Systematic errors or unfair outcomes in AI system results that disproportionately affect specific groups. Sources include unrepresentative training data, flawed assumptions in system design, or feedback loops introduced during deployment. Detecting and mitigating bias is a core AI governance activity.
+
+<span id="eu-ai-act">EU AI Act</span>
+: A regulatory framework introduced by the European Union that classifies AI systems by risk tier^[**European Union:** [Regulation (EU) 2024/1689: Artificial Intelligence Act](https://eur-lex.europa.eu/eli/reg/2024/1689/oj)] — prohibited, high-risk, limited-risk, and minimal-risk — and imposes proportionate obligations such as risk management, data governance, transparency, human oversight, and conformity assessment.
+
+explainability
+: The degree to which the internal mechanics or outputs of an AI system can be understood by humans. Explainability is a core requirement for high-risk AI systems and supports accountability, debugging, and regulatory review.
+
+fairness
+: The principle that AI systems should produce equitable outcomes across individuals and groups. Fairness assessments are a routine part of bias evaluation and impact assessment within AI governance programs.
+
+ISO/IEC 42001
+: An international management system standard for artificial intelligence published by the International Organization for Standardization. Provides requirements for establishing, implementing, maintaining, and continually improving an AI management system within an organization.
+
+model card, system card
+: A standardized document that summarizes an AI system's intended use, training data, performance characteristics, limitations, and ethical considerations. Model and system cards support transparency and informed deployment decisions.^[**Refer also to:** [documentation](#documentation)]
+
+NIST AI Risk Management Framework (AI RMF)
+: A voluntary framework published by the U.S. National Institute of Standards and Technology to help organizations manage risks associated with AI. Organized around four core functions: govern, map, measure, and manage.
+
+responsible AI
+: An umbrella approach to designing, building, and deploying AI systems in ways that are ethical, transparent, accountable, fair, and aligned with human values and societal expectations.
+
+transparency
+: The disclosure of meaningful information about an AI system's design, data, capabilities, limitations, and decision-making processes to relevant stakeholders. Transparency supports trust, accountability, and informed oversight.