AI Generation
Once the collectors bundle your git metadata and GitHub activity, git-brief injects it into a highly constrained System Prompt.
The Bottleneck
Section titled “The Bottleneck”Language models are verbose. If you pass a raw git log to an LLM, it will output a 3-paragraph essay about how “The developer significantly improved the authentication module by refactoring…”.
That is useless for a daily standup. Standups require punchy, action-oriented bullet points.
The System Prompt Rules
Section titled “The System Prompt Rules”To enforce a high-signal, low-noise output, the embedded system prompt enforces strict rules on the LLM:
- Action Verbs Only: Pronouns (“I”) are forbidden. Bullets must start with verbs like “Fixed”, “Reviewed”, or “Pushed”.
- Extreme Specificity: It must extract exact PR numbers (e.g.,
PR #234), branch names, or file paths from the context bundle. - Timestamp Enforcement: It is strictly forbidden from placing a commit dated “Today” into the “Yesterday” bucket.
- Length Limits: Maximum 3 bullet points per section. The total output is hard-capped at 100 words.
Token Costs
Section titled “Token Costs”Because the prompt relies exclusively on metadata (commit messages, PR titles, uncommitted file paths) and never your actual source code, the context window is tiny.
A typical git brief run consumes ~500 input tokens and ~100 output tokens.
| Provider | Model | Cost per Run |
|---|---|---|
| Google Gemini | gemini-2.5-flash |
Free |
| Anthropic | claude-3-5-haiku-latest |
~$0.001 |
| OpenAI | gpt-4o-mini |
~$0.001 |