Empowering Product Owners with Gen AI
Scope
- Automating the generation of user stories.
- Formulating detailed acceptance criteria.
Goals
- Streamline the backlog creation process.
- Reduce manual effort involved in backlog management.
- Enhance the overall quality and relevance of the backlog.
Solution
- Automates routine tasks while keeping decision-making in human hands
- Uses real-world data to maintain relevance and accuracy
- Provides an efficient system for backlog item generation and refinement
High-Level Process
Knowledge Base Creation
Extracts backlog items from Jira and processes them using an embedded model. Converts backlog items into dense vectors, creating a structured and searchable Knowledge Base for future retrieval.
Prompt Formulation
Product owners input a prompt specifying the desired output, such as generating a new user story or refining an existing one.
Data Retrieval (RAG)
The system queries the Knowledge Base using the provided prompt, retrieving relevant backlog items to serve as a context for the AI model.
Context Enrichment
The retrieved data enriches the prompt, adding specific details and context relevant to the ongoing project, which is then fed into the AI model.
Generation (RAG + Prompt Engineering)
The AI model processes the enriched prompt, using domain-specific knowledge and contextual data to generate outputs such as new user stories or acceptance criteria.
Review and Refinement
Product owners review the generated content, making necessary adjustments to align the output with project requirements.
Human-in-the-Loop
Continuous feedback from product owners refines the prompts and improves the system’s accuracy, ensuring critical thinking and human oversight remain integral to backlog management.
Benefits Achieved
The integration of RAG and prompt engineering with Jira delivers substantial benefits that address the core challenges of backlog management:
Factual Accuracy
The use of real backlog data ensures that generated content is grounded in actual project scenarios.
Reduced Bias
By utilizing a curated Knowledge Base, biases introduced through training data are minimized.
Contextual Relevance
The hybrid approach delivers more relevant and detailed outputs, significantly improving the quality of backlog items.
Human Oversight
Ensures critical thinking and decision-making remain central, preventing over-reliance on AI and maintaining high standards of quality in the backlog.