Add latest changes from gitlab-org/gitlab@master

5 files changed, 110 insertions, 17 deletions

diff --git a/doc/ci/components/index.md b/doc/ci/components/index.md
index 3d46ec5bbd5..f7c23964606 100644
--- a/doc/ci/components/index.md
+++ b/doc/ci/components/index.md
@@ -473,3 +473,17 @@ can be converted to a CI/CD component:
      or making it [more efficient](../pipelines/pipeline_efficiency.md).
 1. Leverage the `.gitlab-ci.yml` in the components repository to [test changes to the component](index.md#test-the-component).
 1. Tag and [release the component](index.md#release-a-component).
+
+## Troubleshooting
+
+### `content not found` message
+
+You might receive an error message similar to the following when using the `~latest`
+version qualifier to reference a component hosted by a [catalog resource](catalog.md#add-a-components-repository-to-the-catalog):
+
+```plaintext
+This GitLab CI configuration is invalid: component 'gitlab.com/my-namespace/my-project/my-component@~latest' - content not found`
+```
+
+The `~latest` behavior [was updated](https://gitlab.com/gitlab-org/gitlab/-/issues/429707)
+in GitLab 16.7. It now refers to the latest published version of the catalog resource. To resolve this issue, [create a new release](#release-a-component).
diff --git a/doc/development/ai_features/glossary.md b/doc/development/ai_features/glossary.md
new file mode 100644
index 00000000000..889b5ef4f57
--- /dev/null
+++ b/doc/development/ai_features/glossary.md
@@ -0,0 +1,81 @@
+---
+stage: AI-powered
+group: AI Framework
+info: To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/product/ux/technical-writing/#assignments
+---
+
+# GitLab Duo Glossary
+
+This is a list of terms that may have a general meaning but also may have a
+specific meaning at GitLab. If you encounter a piece of technical jargon related
+to AI that you think could benefit from being in this list, please add it!
+
+- **AI Gateway**: standalone service used to give access to AI features to
+  non-SaaS GitLab users. This logic will be moved to Cloud Connector when that
+  service is ready. Eventually, the AI Gateway will be used to host endpoints that
+  proxy requests to AI providers, removing the need for the GitLab Rails monolith
+  to integrate and communicate directly with third-party LLMs.
+  [Blueprint](../../architecture/blueprints/ai_gateway/index.md).
+- **Chat Evaluation**: automated mechanism for determining the helpfulness and
+  accuracy of GitLab Duo Chat to various user questions. The MVC is an RSpec test
+  run via GitLab CI that asks a set of questions to Chat and then has a
+  two different third-party LLMs determine if the generated answer is accurate or not.
+  [MVC](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/134610).
+  [Design doc for next iteration](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/136127).
+- **Cloud Connector**: Today, Cloud Connector is not a system. It is an umbrella
+  term for all the projects we engage in that make existing SaaS-only features
+  available to self-managed and GitLab Dedicated customers. Today, the only
+  feature available through Cloud Connector is Code Suggestions.
+  Cloud Connector also refer to a planned GitLab-hosted edge service which would
+  act as a way for non-SaaS GitLab instances to access SaaS offerings.
+  [Cloud Connector MVC](../cloud_connector/code_suggestions_for_sm.md).
+  [Blueprint for future Cloud Connector service](../../architecture/blueprints/cloud_connector/index.md).
+- **Consensus Filtering**: method for LLM evaluation where you instruct an LLM
+  to evaluate the output of another LLM based on the question and context that
+  resulted in the output. This is the method of evaluation being used for the Chat
+  Evaluation MVC.
+  [Issue from Model Validation team](https://gitlab.com/gitlab-org/modelops/applied-ml/code-suggestions/prompt-library/-/issues/91#metric-2-consensus-filtering-with-llm-based-evaluation).
+- **Context**: relevant information that surrounds a data point, an event, or a
+  piece of information, which helps to clarify its meaning and implications.
+  For GitLab Duo Chat, context is the attributes of the Issue or Epic being
+  referenced in a user question.
+- **Golden Questions**: a small subset of the types of questions we think a user
+  should be able to ask GitLab Duo Chat. Used to generate data for Chat evaluation.
+  [Questions for Chat Beta](https://gitlab.com/groups/gitlab-org/-/epics/10550#what-the-user-can-ask).
+- **Ground Truth**: data that is determined to be the true
+  output for a given input, representing the reality that the AI model aims to
+  learn and predict. Ground truth data is usually human-annotated.
+- **Model Validation**: group within the AI-powered Stage working on the Prompt
+  Library and researching AI/ML models to support other use-cases for AI at GitLab.
+  [Team handbook section](https://about.gitlab.com/handbook/product/categories/features/#ai-poweredai-model-validation-group).
+- **Prompt library**: The ["Prompt Library"](https://gitlab.com/gitlab-org/modelops/applied-ml/code-suggestions/prompt-library) is a Python library that provides a CLI for testing different prompting techniques with LLMs. It enables data-driven improvements to LLM applications by facilitating hypothesis testing. Key features include the ability to manage and run dataflow pipelines using Apache Beam, and the execution of multiple evaluation experiments in a single pipeline run. 
+  on prompts with various third-party AI Services.
+  [Code](https://gitlab.com/gitlab-org/modelops/applied-ml/code-suggestions/prompt-library).
+- **Prompt Registry**: stored, versioned prompts used to interact with third-party
+  AI Services. [Blueprint](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/135872).
+- **Prompt**: instructions sent to an LLM to perform certain tasks. [Prompt guidelines](prompts.md).
+- **RAG Pipeline**: (Retrieval-Augmented Generation) is a mechanism used to take
+  an input (such as a user question) into a system, retrieve any relevant data
+  for that input, augment the input with additional context, and then
+  synthesize the information to generate a coherent, contextualy-relevant answer.
+  This design pattern is helpful in open-domain question answering with LLMs,
+  which is why we use this design pattern for answering questions to GitLab Duo Chat.
+- **Similarity Score**: method to determine the likeness between answers produced by an LLM and the reference ground truth answers.
+  [Issue from Model Validation team](https://gitlab.com/gitlab-org/modelops/applied-ml/code-suggestions/prompt-library/-/issues/91#metric-1-similarity-score-as-comparisons-for-llms).
+- **Tool**: logic that performs a specific LLM-related task; each tool has a
+  description and its own prompt. [How to add a new tool](duo_chat.md#adding-a-new-tool).
+- **Word-Level Metrics**: method for LLM evaluation that compares aspects of
+  text at the granularity of individual words.
+  [Issue from Model Validation team](https://gitlab.com/gitlab-org/modelops/applied-ml/code-suggestions/prompt-library/-/issues/98#metric-3-word-level-metrics).
+- **Zero-shot agent**: in the general world of AI, a learning model or system
+  that can perform tasks without having seen any examples of that task during
+  training. At GitLab, we use this term to refer specifically to a piece of our
+  code that serves as a sort of LLM-powered air traffic controller for GitLab Duo Chat.
+  The GitLab zero-shot agent has a system prompt that explains how an LLM should
+  interpret user input from GitLab Duo Chat as well as a list of tool descriptions.
+  Using this information, the agent determines which tool to use to answer a
+  user's question. The agent may decide that no tools are required and answer the
+  question directly. If a tool is used, the answer from the tool is fed back to
+  the zero-shot agent to evaluate if the answer is sufficient or if an additional
+  tool must be used to answer the question.
+  [Code](https://gitlab.com/gitlab-org/gitlab/-/blob/6b747cbd7c6a71145a8bfb8201db3c857b5aed6a/ee/lib/gitlab/llm/chain/agents/zero_shot/executor.rb). [Zero-shot agent in action](https://gitlab.com/gitlab-org/gitlab/-/issues/427979).
diff --git a/doc/development/ai_features/index.md b/doc/development/ai_features/index.md
index 87c8abea405..aa8dc4715c7 100644
--- a/doc/development/ai_features/index.md
+++ b/doc/development/ai_features/index.md
@@ -37,7 +37,7 @@ Apply the following two feature flags to any AI feature work:
 - A general flag (`ai_global_switch`) that applies to all AI features.
 - A flag specific to that feature. The feature flag name [must be different](../feature_flags/index.md#feature-flags-for-licensed-features) than the licensed feature name.
 
-See the [feature flag tracker](https://gitlab.com/gitlab-org/gitlab/-/issues/405161) for the list of all feature flags and how to use them.
+See the [feature flag tracker epic](https://gitlab.com/groups/gitlab-org/-/epics/10524) for the list of all feature flags and how to use them.
 
 ## Implement a new AI action
 
@@ -61,10 +61,10 @@ Use [this snippet](https://gitlab.com/gitlab-org/gitlab/-/snippets/2554994) for
 
 1. Ensure you have followed [the process to obtain an EE license](https://about.gitlab.com/handbook/developer-onboarding/#working-on-gitlab-ee-developer-licenses) for your local instance
 1. Simulate the GDK to [simulate SaaS](../ee_features.md#simulate-a-saas-instance) and ensure the group you want to test has an Ultimate license
-1. Enable `Experimental features`:
+1. Enable `Experiment & Beta features`
    1. Go to the group with the Ultimate license
    1. **Group Settings** > **General** -> **Permissions and group features**
-   1. Enable **Experiment features**
+   1. Enable **Experiment & Beta features**
 1. Enable the specific feature flag for the feature you want to test
 1. Set the required access token. To receive an access token:
    1. For Vertex, follow the [instructions below](#configure-gcp-vertex-access).
@@ -72,9 +72,6 @@ Use [this snippet](https://gitlab.com/gitlab-org/gitlab/-/snippets/2554994) for
 
 ### Set up the embedding database
 
-NOTE:
-Use [this snippet](https://gitlab.com/gitlab-org/gitlab/-/snippets/2554994) for help automating the following section.
-
 For features that use the embedding database, additional setup is needed.
 
 1. Enable [`pgvector`](https://gitlab.com/gitlab-org/gitlab-development-kit/-/blob/main/doc/howto/pgvector.md#enable-pgvector-in-the-gdk) in GDK
diff --git a/doc/user/packages/container_registry/reduce_container_registry_storage.md b/doc/user/packages/container_registry/reduce_container_registry_storage.md
index b4e37714a5d..75dfa13a9d8 100644
--- a/doc/user/packages/container_registry/reduce_container_registry_storage.md
+++ b/doc/user/packages/container_registry/reduce_container_registry_storage.md
@@ -15,12 +15,17 @@ if you add a large number of images or tags:
 You should delete unnecessary images and tags and set up a [cleanup policy](#cleanup-policy)
 to automatically manage your container registry usage.
 
-## Check Container Registry storage use **(FREE SAAS)**
+## View container registry usage **(FREE SAAS)**
 
-The Usage Quotas page (**Settings > Usage Quotas > Storage**) displays storage usage for Packages.
-Measuring usage is only possible on the new version of the GitLab Container Registry backed by a
-metadata database, which is [available on GitLab.com](https://gitlab.com/groups/gitlab-org/-/epics/5523) since GitLab 15.7.
-For information on the planned availability for self-managed instances, see [epic 5521](https://gitlab.com/groups/gitlab-org/-/epics/5521).
+> [Introduced](https://gitlab.com/groups/gitlab-org/-/epics/5523) in GitLab 15.7
+
+To view the storage usage for the container registry:
+
+1. On the left sidebar, select **Search or go to** and find your project.
+1. Select **Settings > Usage Quotas**.
+
+You cannot view Container Registry usage for self-managed instances, but this is
+proposed in [epic 5521](https://gitlab.com/groups/gitlab-org/-/epics/5521).
 
 ## How container registry usage is calculated
 
diff --git a/doc/user/product_analytics/index.md b/doc/user/product_analytics/index.md
index 245ffc34783..db52f9e51cc 100644
--- a/doc/user/product_analytics/index.md
+++ b/doc/user/product_analytics/index.md
@@ -299,12 +299,8 @@ If the request is successful, the returned JSON includes an array of rows of res
 
 ## View product analytics usage quota
 
-> [Introduced](https://gitlab.com/gitlab-org/gitlab/-/issues/424153) in GitLab 16.6 with a [flag](../../administration/feature_flags.md) named `product_analytics_usage_quota`. Disabled by default.
-
-FLAG:
-On self-managed GitLab, by default this feature is not available. To make it available per project or for your entire instance, an administrator can [enable the feature flag](../../administration/feature_flags.md) named `product_analytics_usage_quota`.
-On GitLab.com, this feature is not available.
-This feature is not ready for production use.
+> - [Introduced](https://gitlab.com/gitlab-org/gitlab/-/issues/424153) in GitLab 16.6 with a [flag](../../administration/feature_flags.md) named `product_analytics_usage_quota`. Disabled by default.
+> - [Generally available](https://gitlab.com/gitlab-org/gitlab/-/issues/427838) in GitLab 16.7. Feature flag `product_analytics_usage_quota` removed.
 
 Product analytics usage quota is calculated from the number of events received from instrumented applications.
 The tab displays the monthly totals for the group, and a breakdown of usage per project. Current month shows events counted to date.