Summary
Use an LLM to generate a structured Pugh decision matrix from an Architecture Decision Record (ADR) as input. The LLM should extract or infer:
- Options being compared (e.g., Zustand vs. Redux vs. Jotai)
- Criteria relevant to the decision (e.g., bundle size, learning curve, dev experience)
- Weights for each criterion based on context in the ADR
- Ratings for each option against each criterion
Motivation
ADRs already capture the reasoning behind architectural decisions, but translating that into a structured comparison matrix is manual and tedious. LLM-assisted generation lets teams get a first-draft Pugh matrix instantly, which they can then refine.
Proof of Concept
Eli is building an initial POC using the Zustand evaluation ADR as the test case. This issue captures the full feature scope beyond that POC.
Considerations
- Input format: markdown ADR (could support multiple ADR templates)
- Output: a valid Pugh matrix data structure compatible with the existing component
- Should support iterative refinement — user can adjust the generated matrix after creation
- Prompt engineering and model selection TBD
Related
Summary
Use an LLM to generate a structured Pugh decision matrix from an Architecture Decision Record (ADR) as input. The LLM should extract or infer:
Motivation
ADRs already capture the reasoning behind architectural decisions, but translating that into a structured comparison matrix is manual and tedious. LLM-assisted generation lets teams get a first-draft Pugh matrix instantly, which they can then refine.
Proof of Concept
Eli is building an initial POC using the Zustand evaluation ADR as the test case. This issue captures the full feature scope beyond that POC.
Considerations
Related