Iteration #2/Effective Prompt Debugging for Quick and Strategic Improvement

[krisnaBukitVista]

Description

We have developed Tinker, a powerful tool designed for prompt debugging. Our current objective is to drive the adoption of Tinker among users, ensuring it becomes an integral part of their daily activities.

Problem

• Despite its capabilities, user adoption of Tinker remains low. To address this, we need AI supervisors to actively use Tinker and seamlessly integrate it into their workflows.
• We got minor bug to fix which also influence the user comfortability

Solutions

• Interface Improvement: We will enhance the Tinker interface to make it more intuitive and human-readable, ensuring a smoother user experience.
• Operational Supervision: During their BBW (Build Better Week), we will closely supervise operations teams to engineer and create targeted goals. Each team will be tasked with solving at least five real cases using prompt debugging with Tinker.
• In order to replicate it perfectly, we need to integrate GPT 4o on tinker and solve issue with status code 500 which frequently occur on model GPT 3.5. Check the task here

Measurement metrics

• Daily Report Submissions
• Category Enhancements

SLA

• Daily Report Submissions: We expect to receive an average of at least one report per day from users during the evaluation period.
• Category Enhancements: Users should propose at least 14 category enhancements based on their experiences and findings with Tinker.

Team Members

@krisnaBukitVista

[krisnaBukitVista]

Updates on June 30th

• 80 reports during evaluation process
• 15 categories description enhancement proposal

[Vidiskiu]

Overall Point: 5.1

Functional Complexity: 1

Iterative improvement of the Tinker tool requires multifaceted functional changes including user interface updates, workflow integration, and handling specific bugs.

Technical Complexity: 1.1

Incorporating AI supervision, interfacing improvements, and bug fixes adds to the technical complexity, although the specifics are somewhat mitigated by existing frameworks and tools.

UI/UX Complexity: 0.8

UI/UX improvements are critical for user adoption, which means translating technical aspects into intuitive design, but the scope is limited to Tinker's interface.

Data Manipulation: 0.5

Debugging and enhancing feature categories will involve data manipulation, but this seems standardized without significant additional complexities.

Testing: 0.3

Testing will be needed to ensure smooth operation of the new features and bug fixes, but it falls within the normal range of QA processes.

Dependencies: 0.4

Dependencies might include external AI tools or libraries, specifically the integration of GPT-4. However, the issue description indicates work on known frameworks.

Risk and Uncertainty: 0.5

There are risks associated with user adoption and potential unforeseen issues when integrating new AI models, but these are mitigated by current knowledge of the system and AI tools.

User Impact: 0.5

Enhancing user adoption of Tinker is significant for user effectiveness. However, the current user base appears to be limited to AI supervisors, which suggests a more targeted impact.