Project Description
Developing a web apps prototype using LLM-RAG architecture to analyze DORA metrics and provide actionable insights for stakeholders. This tool is expected to process DORA metrics including deployment frequency, lead time, change failure rate, and time to restore service metrics to support analysis, to identify bottlenecks and improvement opportunities. It uses a sophisticated RAG system to provide context-aware recommendations based on industry best practices and historical data.
Detailed Tasks
- Designed a web app prototype using Python and Flask to provide a user-friendly interface for analyzing DORA metrics. Enable natural language input and display of LLM-generated insights
- Implemented a basic RAG-based architecture for processing and analyzing DORA metrics
Detailed Tasks (expected to be completed)
- Design and implement a full fledged RAG-based architecture for processing and analyzing DORA metrics and generating context-aware recommendations
- Develop custom embeddings for technical documentation and best practices
- Create an automated data pipeline for collecting and processing metrics from various sources
- Implement a recommendation engine using LLM for generating actionable insights
Business Impact
- Expected reduction in analysis time by 60% through automated DORA metrics processing
- Enabled natural language data-driven decision making with context-aware insights and recommendations
Expected Project Outcome
- Enhanced accuracy and efficiency in DORA metrics analysis through advanced LLM and RAG techniques.
- Improved decision-making capabilities for stakeholders by providing actionable insights derived from complex data sets.