Problem
Teams with sensitive code need AI help without sending data to hosted providers.
Project Case Study
Local LLM Coding Assistant
Developer assistant focused on private codebases and policy-aware suggestions.
PlannedPythonLocal ModelsVS CodeVector Search
Goal
Enable local inference with useful context retrieval and development ergonomics.
Architecture Overview
System shape and flow
- Local embedding and retrieval index for repo context
- On-device model execution with prompt templates
- Tooling layer for code actions and diagnostics
Key Features
- Local inference
- Repo context retrieval
- Policy-aware prompting
Tradeoffs and Design Decisions
- Lower model quality than top hosted models
- Higher local compute requirements
Challenges
- Resource management on developer machines
- Balancing latency and context depth
Results and Lessons Learned
- Planned baseline benchmarks defined
- Initial architecture documented
Next Steps
- Implement first coding workflows
- Run benchmark matrix