Project 02
15,000 Questions
An analysis of historical BI Copilot conversations to improve intent handling, repeated-question processing, caching, routing, and role-aware responses.
- COMPLETED CONTRIBUTION
- CONFIDENTIAL CONTEXT
- Category
- Analytics / GenAI / Business Intelligence
- Role
- Computer Vision Intern
- Maturity
- Completed contribution
- Year
- 2026
30-second proof
What this proves
- Problem
- Business users expressed similar analytical needs through many different question formats, creating repeated intents and failure patterns.
- Operational or business context
- Internal BI Copilot and Snowflake-based business analytics environment.
- My contribution
- I analyzed historical conversations, identified intent and failure patterns, and contributed to prompt, caching, routing, and semantic-view improvements.
- Constraints
- Confidential conversations · Role-dependent business questions · Repeated user intents · Proprietary prompts and internal schemas
- Project maturity
- Completed contribution
- Contextual technical stack
- Snowflake · SQL · Snowflake Cortex · Intent analysis
Measurement noteThe portfolio must not show real conversations, proprietary prompts, semantic-view structures, or internal datasets.
Ownership
Contributed to
- I contributed to intent, prompt, caching, routing, and semantic-view improvements.
Collaborative context
- Internal BI Copilot and business analytics context.
Existing environment
- Snowflake-based internal business analytics environment.
Not shown publicly
- Real conversations, proprietary prompts, semantic-view structures, and internal datasets are not shown.
Overview
An analysis of historical BI Copilot conversations to improve intent handling, repeated-question processing, caching, routing, and role-aware responses.
The problem
Similar business questions can arrive with different wording, roles, and context, which complicates reliable response handling.
Context
The work used confidential historical business-intelligence conversations and must not expose internal data.
My role
I worked on this project as a Computer Vision Intern.
What I built
I analyzed conversation patterns and contributed to improvements in intent handling, repeated-question processing, caching, routing, and role-aware responses.
How it works
Historical questions were reviewed to identify repeated needs, routing patterns, and response-handling improvements.
- Historical conversations
- Intent patterns
- Failure analysis
- Routing improvements
- Better responses
Decisions
- I analyzed historical behavior before proposing changes.
- I grouped repeated intents and failure patterns.
- I separated role-aware business needs rather than treating every question identically.
Evidence and results
The verified evidence is limited to the scale of historical conversations analyzed.
The result should be read as a contribution to identifying repeated intents and failure patterns, not as a public claim about internal model performance.
The portfolio must not show real conversations, proprietary prompts, semantic-view structures, or internal datasets.
Challenges and limitations
Internal conversations, semantic views, proprietary prompts, datasets, and query structures cannot be shown.
Internal conversations, semantic views, proprietary prompts, datasets, and query structures are not shown.
What I would improve next
I would add sanitized examples of repeated question patterns if they are approved for public use.