Customer support and project management are critical factors in providing effective customer relationship management. Atlassian Jira, a platform for issue tracking and project management features for software projects, has become an essential part of many organizations’ workflows, ensuring customer and product success. However, extracting valuable information from the vast amount of data stored in Jira often requires manual efforts and the building of specialized tools. Users such as support engineers, project managers, and product managers need to be able to ask questions about a project, issue, or customer in order to provide excellent customer care. Generative AI offers the ability to take relevant information from a data source and provide well-constructed answers to the user.
Building a generative AI-based conversational application that is integrated with data sources containing relevant content that a company needs requires time, money, and personnel. Firstly, connectors to data sources need to be built. Then, this data must be indexed so that it is available through an Augmented Retrieval Generation (RAG) approach, where relevant passages are delivered with high accuracy to a large language model (LLM). This requires selecting an index that provides indexing capabilities for semantic and vectorial search, building the infrastructure to retrieve and rank answers, and building a feature-rich web application. It also requires hiring and staffing a large team to build, maintain, and manage such a system.
Amazon Q Business is a fully managed, generative AI-powered assistant that can answer questions, provide summaries, generate content, and perform tasks securely based on data and information in enterprise systems. Amazon Q Business can help get quick and relevant answers to pressing questions, solve issues, generate content, and take actions using data and knowledge found in enterprise information repositories, code, and business systems like Jira, among others. Amazon Q provides native data source connectors that can index content in a built-in retriever and use an LLM to provide accurate and well-written answers. A data source connector is a component of Amazon Q that helps integrate and synchronize data from multiple repositories into a single index.
Amazon Q Business offers multiple pre-defined connectors to numerous data sources, including Atlassian Jira, Atlassian Confluence, Amazon Simple Storage Service (Amazon S3), Microsoft SharePoint, Salesforce, and many more, and helps create a generative AI solution with minimal configuration. This post describes the setup and integration of Amazon Q for Business with Jira to enable support teams, project management, product management, leadership, and others to quickly get accurate answers to their content-related questions in Jira projects, issues, and more.
After integrating Amazon Q Business with Jira, users can ask questions based on document descriptions. This allows for the following use cases:
– Natural language search: users can search for tasks, issues, or other project-related information using conversational language, making it easier to find desired data without the need to remember keywords or specific filters.
– Summarization: users can request a concise summary of all issues, tasks, or other entities matching their search query, allowing them to quickly grasp key points without having to manually review individual document descriptions.
– Query clarification: if a user’s query is ambiguous or lacks sufficient context, Amazon Q Business can engage in a dialogue to clarify intent, so that the user receives the most relevant and accurate results.
To track and index content in Jira, the Jira connector of Amazon Q Business can be configured as a data source in the Amazon Q Business application. By connecting Amazon Q Business to a data source and starting the synchronization process, Amazon Q Business tracks and indexes documents from the data source into its index.
In Amazon Q Business, a document is a unit of data consisting of a title, content (or body), metadata (data about the document), and access control list (ACL) information to ensure that responses are provided from documents that the user has access to. The Jira connector of Amazon Q Business supports tracking the following entities in Jira: projects, issues, comments, attachments, and work logs. Additionally, Jira users can create custom objects and custom metadata fields, and Amazon Q supports tracking and indexing of these custom objects and metadata.
Referring to relevant guides can provide more information on how to set up this integration and maximize Amazon Q Business capabilities for more effective project and customer support management.
Referrer: MiMub in Spanish
