Product Specifications
Turn high-level opportunity buckets into concrete Product Requirements Documents (PRDs) and engineering specs with AI.
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EPIC SPECIFICATION
AI-Driven Requirement Analysis and Generation
Description
Develop a system that aggregates raw data from various sources and employs AI to identify top opportunities and generate well-structured requirements, improving project management and planning.
Expected Impact
This feature will provide users with predictability and reduce ambiguity in planning by efficiently analyzing inputs from diverse sources, thus transforming them into actionable insights and structured epics, ultimately improving user satisfaction and efficiency in project management.
User Stories
Aggregate and analyze raw inputsLAs a project manager, I want to automatically aggregate and analyze raw inputs from multiple sources so that I can identify top opportunities without manual intervention.
This story involves developing functionality that will collect data from specified channels, apply NLP, and other AI techniques to extract insights and present them as potential opportunities or areas of focus.
- Support input from call transcripts and support tickets.
- Capable of processing and identifying key themes and opportunities.
- Implement NLP library for text analysis.
- Design a scalable data aggregation API.
- Aggregated data from >=3 input sources is processed to show at least 5 opportunities.
- Opportunities should be displayed with a confidence level based on AI analysis.
Develop API for data aggregation
Create an API that collects data from different sources, aggregates it, and forwards it for processing.
Implement NLP and AI solutions
Utilize libraries such as SpaCy or NLTK to process aggregated data and identify insights.
Generate structured requirements from opportunitiesMAs a project manager, I want to convert identified opportunities into structured epics and user stories for better planning and execution.
This story requires building a system that takes the identified opportunities and automatically structures them into actionable epics and user stories to facilitate better planning and tracking.
- Enable conversion of AI-generated opportunities into epics and user stories.
- Allow users to refine and edit suggestions before finalizing.
- Develop a conversion algorithm within the backend.
- Integrate a frontend module for user interaction.
- Opportunities are converted into at least one epic with >= 3 user stories each.
- Users can edit the converted requirements before saving them.
Develop requirement conversion logic
Implement the logic to translate opportunities into structured epics and user stories.
Create frontend for requirement interaction
Design a user interface component enabling users to view and refine generated requirements.
Acceptance Criteria
- Given aggregated inputs, when processed through the system, then the result should be structured requirements derived from identified opportunities.
Global Epic Technical Scope
Requires development of a scalable data aggregation and processing service, integration of NLP and AI for insight extraction, backend logic for requirement structuring, and frontend UI for interaction.
EPIC SPECIFICATION
AI Insight Traceability and Explainability
Description
Enable users to understand, verify, and trust AI-generated insights and recommendations by exposing the evidence, rationale, confidence level, assumptions, and limitations behind each generated output.
Expected Impact
This directly addresses user distrust by making AI outputs auditable, explainable, and verifiable against original recordings, transcripts, logs, and user feedback, reducing the need for manual requirement extraction.
User Stories
View evidence behind AI-generated insightsLAs a product user, I want to see the source evidence behind an AI-generated insight so that I can verify whether the insight is accurate.
Users should be able to inspect the original supporting evidence for each AI-generated insight, including transcript excerpts, recording timestamps, log snippets, feedback items, or other source artifacts. The evidence should be presented in a way that makes it clear why the AI created the insight and how strongly the evidence supports it.
- Each AI-generated insight must include at least one supporting evidence reference when source material is available.
- Evidence references must include source type, source title or identifier, excerpt text, and timestamp or location when applicable.
- Users must be able to expand an insight to view its supporting evidence.
- Users must be able to navigate from an evidence item back to the original source artifact when permissions allow.
- If no evidence is available, the UI must clearly state that the insight has insufficient traceability rather than implying certainty.
- Add an evidence reference model that supports source type, source ID, excerpt, timestamp range, confidence contribution, and metadata.
- Update AI generation pipelines to return structured citations alongside generated insights.
- Persist evidence references atomically with generated insight records.
- Expose evidence references through existing or new insight detail APIs.
- Ensure source navigation respects existing access controls and workspace permissions.
- Given an AI-generated insight with supporting transcript excerpts, when the user expands the insight, then the user sees the relevant excerpts and their timestamps.
- Given an evidence item linked to a recording, when the user selects the evidence link, then the user is taken to the corresponding recording location if they have permission.
- Given an insight without supporting evidence, when the insight is displayed, then the UI clearly labels it as having limited traceability.
- Given a user without access to a linked source artifact, when they view the insight evidence, then restricted evidence is hidden or redacted according to permission rules.
Define and persist insight evidence references
Create the backend data model and persistence logic required to associate each AI-generated insight with structured evidence references, including source ID, source type, excerpt text, timestamp or location metadata, and permission-aware source linkage.
Update AI insight generation to emit citations
Modify the AI generation workflow so that generated insights include structured citation data tying each insight back to the source chunks, transcript excerpts, logs, or feedback records used to generate it.
Expose evidence through insight APIs
Update insight retrieval APIs to include evidence references in the response payload, ensuring evidence is filtered or redacted based on the requesting user's permissions.
Build expandable evidence UI
Add an expandable evidence section to the insight detail and insight list views so users can inspect supporting excerpts, source labels, timestamps, and links back to the original artifact.
Explain AI recommendation rationaleMAs a product decision-maker, I want AI recommendations to explain their reasoning so that I can judge whether to act on them.
AI-generated recommendations should include a concise rationale that explains the reasoning path from evidence to recommendation. The rationale should distinguish between observed facts, inferred implications, and suggested actions so users can understand what the AI knows versus what it assumes.
- Each AI-generated recommendation must include a rationale summary.
- The rationale must separate observed evidence, inferred user need, and recommended action.
- Recommendations must include the primary insight or insight cluster that triggered them.
- The UI must display rationale in a readable structure that avoids opaque AI-generated paragraphs.
- Users must be able to identify which parts of a recommendation are evidence-based versus inferred.
- Update recommendation generation schema to require rationale fields for observed facts, inferred implications, and recommended action.
- Validate AI responses before persistence to ensure required rationale fields are present.
- Add fallback handling for malformed or incomplete AI rationale outputs.
- Extend recommendation API payloads to include structured rationale fields.
- Instrument generation failures where rationale cannot be produced or validated.
- Given an AI-generated recommendation, when the user opens it, then they see a structured rationale explaining observed facts, inferred need, and recommended action.
- Given a recommendation generated from multiple insights, when the rationale is shown, then the source insight cluster or contributing insights are visible.
- Given the AI returns an incomplete rationale, when the recommendation is saved, then the system either retries generation or marks the rationale as incomplete.
- Given a user reviews the recommendation, when they compare rationale to evidence, then they can distinguish factual support from AI inference.
Update recommendation generation schema
Revise the AI recommendation output contract so every recommendation must include structured rationale fields for observed evidence, inferred implication, and recommended action.
Add rationale validation and fallback handling
Implement backend validation to detect missing or malformed rationale fields and add retry, fallback, or incomplete-state handling before recommendations are displayed to users.
Persist and expose recommendation rationale
Update recommendation storage and API responses to include structured rationale data and references to the contributing insights or insight clusters.
Create rationale display component
Build a frontend component that displays recommendation rationale in clearly labeled sections so users can quickly understand the reasoning behind the recommendation.
Show confidence and uncertainty signalsLAs a user reviewing AI outputs, I want to see confidence and uncertainty indicators so that I know when to trust, question, or manually verify an insight.
AI-generated insights and recommendations should communicate confidence in a transparent way. Instead of presenting all outputs as equally reliable, the system should show confidence level, evidence strength, conflicting signals, missing data, and whether human review is recommended.
- Each AI-generated insight and recommendation must display a confidence level such as High, Medium, or Low.
- Confidence must be based on explainable factors such as evidence volume, consistency, source quality, and presence of conflicting signals.
- Low-confidence outputs must be visually flagged for review.
- The UI must explain why a confidence level was assigned.
- Outputs with conflicting evidence must show a conflict or uncertainty note.
- Define a confidence scoring rubric that can be computed from evidence count, source diversity, semantic consistency, recency, and conflict detection.
- Store confidence score, confidence label, and confidence explanation with each AI output.
- Update generation and post-processing workflows to calculate and persist confidence metadata.
- Expose confidence metadata through APIs consumed by insight and recommendation views.
- Add UI states for high-confidence, medium-confidence, low-confidence, and conflicting-evidence outputs.
- Given an insight supported by many consistent evidence items, when it is displayed, then it shows a high-confidence indicator with an explanation.
- Given an insight supported by only one ambiguous source, when it is displayed, then it shows a low-confidence indicator and recommends review.
- Given conflicting source evidence, when an AI output is displayed, then the UI shows an uncertainty or conflict note.
- Given a user views the confidence indicator, when they expand it, then they see the factors that contributed to the confidence level.
Define confidence scoring rubric
Create a documented scoring approach that assigns confidence based on evidence count, evidence consistency, source diversity, source quality, recency, and detected contradictions.
Implement confidence metadata calculation
Add backend logic to calculate confidence score, label, and explanation during AI insight and recommendation generation or post-processing.
Persist and return confidence metadata
Update storage and API layers so each AI-generated insight and recommendation includes confidence score, confidence label, and confidence explanation.
Add confidence indicators to the UI
Build visual indicators and expandable explanations for confidence levels, including distinct handling for low-confidence and conflicting-evidence states.
Capture human feedback on AI outputsMAs a reviewer, I want to mark AI insights as accurate, inaccurate, or incomplete so that the system can reflect human validation and improve trust.
Users should be able to provide lightweight validation feedback on AI-generated insights and recommendations. This feedback should be visible to teammates and available for future quality analysis, allowing teams to distinguish reviewed AI outputs from unverified ones.
- Users must be able to mark an AI-generated insight or recommendation as accurate, inaccurate, or incomplete.
- Users must be able to optionally add a comment explaining their feedback.
- The system must show whether an AI output has been reviewed by a human.
- The system must display reviewer identity and review timestamp where appropriate.
- Feedback must be available for reporting on AI output quality.
- Add a feedback model for AI output validation status, reviewer ID, comment, timestamp, and target output ID.
- Create APIs to create, update, and retrieve validation feedback.
- Ensure only authorized users can submit or modify validation feedback.
- Add frontend controls for submitting validation feedback from insight and recommendation views.
- Emit analytics events when feedback is submitted or updated.
- Given an unreviewed AI insight, when a user marks it as accurate, then the insight displays a reviewed status with reviewer and timestamp.
- Given a recommendation marked incomplete, when another teammate views it, then they see the incomplete status and reviewer comment.
- Given a user without edit permissions, when they view an AI output, then they cannot submit or modify validation feedback.
- Given feedback has been submitted, when quality analytics are queried, then the feedback is available for aggregate reporting.
Create AI output validation feedback model
Add backend storage for validation status, optional reviewer comment, reviewer identity, timestamp, and the associated insight or recommendation ID.
Build validation feedback APIs
Implement permission-protected endpoints for creating, updating, and retrieving human validation feedback on AI-generated outputs.
Add review controls to insight and recommendation views
Create frontend controls that allow authorized users to mark AI outputs as accurate, inaccurate, or incomplete and optionally provide explanatory comments.
Add reviewed status indicators and analytics
Display human review state to other users and emit analytics events for feedback submission and updates so AI quality can be monitored over time.
Acceptance Criteria
- Given an AI-generated insight, when a user opens its detail view, then they can see supporting evidence, rationale, confidence level, and any human validation status.
- Given an AI-generated recommendation, when a user reviews it, then they can understand the evidence and reasoning that led to the recommendation.
- Given source evidence exists for an insight, when a user selects an evidence reference, then they can navigate to the original source artifact if they have permission.
- Given an AI output has weak or conflicting evidence, when it is displayed, then the system clearly communicates uncertainty and recommends review.
- Given a user validates an AI output, when teammates view the output, then the human review status is visible.
- Given a user lacks permission to underlying source data, when they view AI traceability details, then restricted source evidence is hidden or redacted.
Global Epic Technical Scope
This epic requires updates across AI generation workflows, data persistence, APIs, permissions, frontend insight and recommendation views, and analytics. The system must persist structured evidence references, rationale fields, confidence metadata, and human validation feedback for AI-generated insights and recommendations. APIs must expose this metadata in permission-aware ways, while the frontend must present traceability information through expandable evidence panels, rationale sections, confidence indicators, and review controls.