braintrustdata/braintrust-cookbook
Jun 2026 – Jun 2026
1 Month active
Languages Used
JSONPython
Technical Skills
AI DevelopmentData AnalysisMachine LearningPython Programming
DaRubberDuckieee
PROFILE
Darubberduckieee
Developed the AI Support Chatbot Troubleshooting Cookbook for the braintrust-cookbook repository, providing a structured, data-driven workflow for diagnosing and resolving failures in customer-support chatbots. Leveraging Python and data analysis techniques, the work detailed project setup for a Supabase-backed chatbot with tool calling, systematic log generation, and classification of failure modes using Braintrust Topics. The cookbook guided users through clustering five concrete failure types, curating evaluation datasets, and establishing an offline evaluation framework to compare model versions. This approach enabled reproducible triage environments and prepared teams for effective deployment monitoring, supporting continuous improvement in AI-driven support scenarios without direct bug fixes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
89,919
Activity Months1
Your Network
12 peopleWork History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026: Delivered the AI Support Chatbot Troubleshooting Cookbook for braintrust-cookbook, enabling systematic triage of AI failures in a customer-support chatbot using Braintrust Topics. The cookbook guides users from project setup (Supabase-backed chatbot with tool calling) through log generation, failure classification, clustering of 5 concrete failure modes, curated eval data creation, offline evaluation (v1 → v2), to deploying fixes and monitoring with fresh logs. This work establishes a repeatable, data-driven playbook that accelerates issue discovery, reproducibility, and remediation in live support scenarios.
1 Commits • 1 Features
Jun 1, 2026June 2026: Delivered the AI Support Chatbot Troubleshooting Cookbook for braintrust-cookbook, enabling systematic triage of AI failures in a customer-support chatbot using Braintrust Topics. The cookbook guides users from project setup (Supabase-backed chatbot with tool calling) through log generation, failure classification, clustering of 5 concrete failure modes, curated eval data creation, offline evaluation (v1 → v2), to deploying fixes and monitoring with fresh logs. This work establishes a repeatable, data-driven playbook that accelerates issue discovery, reproducibility, and remediation in live support scenarios.
June 2026
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
JSONPython
Technical Skills
AI DevelopmentData AnalysisMachine LearningPython Programming
Repositories Contributed To
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