Timeline
- 09:00 (Context) Set the goal: turn the AI Sentinel prompts into a repeatable, file-first pipeline.
- 09:20 (Action) Scaffolded the working structure and clarified what counts as inputs, intermediate artifacts, and outputs.
- 09:45 (Action) Drafted the first genesis executors to sequence Stage 2 (seed extraction) into Stage 3 (filtration analysis).
- 10:10 (Observation) A single-entry input convention dramatically simplifies automation and reduces operator choices.
- 10:30 (Action) Added toggle-able behavior (auto vs. pause-for-review) so early runs can calibrate quality without losing speed later.
- 11:05 (Action) Standardized “file-first” behavior: minimal chat output, everything written to deterministic locations.
- 11:40 (Action) Ran the first end-to-end test (Run A), capturing all intermediate artifacts for inspection.
- 12:10 (Observation) The earliest outputs revealed wording drift between the seed structure and the filtration prompt’s expectations.
- 12:35 (Action) Tightened seed schema alignment and adjusted the genesis flow to reduce ambiguity between stages.
- 13:20 (Action) Ran the second end-to-end test (Run B) with slight variation to pressure-test robustness and surface edge cases.
- 13:55 (Observation) Small instruction variations produce meaningful differences in clarity, especially around scoring and decision thresholds.
- 14:20 (Action) Consolidated learnings into evolved prompt/guidance files while preserving test artifacts as frozen evidence.
- 14:50 (Open Thread) Decide what is truly “core” vs. “experimental” so the live system stays lean while insights remain accessible.
Context
- 09:00 Building a prompt-based system that behaves like a tool: deterministic inputs/outputs and low operator overhead.
- 09:05 Genesis executors were created early; the rest of the system evolved through iteration after real runs.
- 09:10 Two exploratory test runs were used to compare outputs and cultivate insights from small procedural changes.
- 09:15 Constraint: prioritize “seeds, not maintenance” and keep the workflow lightweight and repeatable.
Actions
- 09:20 Created/validated a clear folder model to separate raw inputs, extracted seeds, and analysis outputs.
- 09:45 Implemented genesis executors to orchestrate Stage 2 -> Stage 3 reliably.
- 10:30 Introduced a review toggle so the pipeline can pause after seed generation when calibration is needed.
- 11:05 Enforced file-first output conventions and consistent naming to reduce cognitive load across runs.
- 11:40 Executed Run A and preserved all emitted artifacts for review.
- 12:35 Updated prompt phrasing/structure to reduce mismatch between the seed format and filtration expectations.
- 13:20 Executed Run B with slight variations to confirm the system tolerates instruction changes.
- 14:20 Folded the deltas back into the live workflow while keeping the test artifacts untouched.
Observations
- 10:10 “One input at a time” conventions support near-zero-UI automation and reduce failure modes.
- 12:10 Output quality depends heavily on schema consistency across prompts (seed fields must match filtration reading habits).
- 13:55 The review toggle is essential early: it prevents compounding errors while prompts are still settling.
- 14:20 Treating test outputs as immutable evidence makes iteration more scientific (compare, don’t reinvent).
Open Threads
- 14:50 Decide where run logs vs. reference examples should live long-term (naming + folder semantics).
- 14:55 Consider a lightweight rubric/checklist for seed completeness before allowing Stage 3 to run unattended.
- 15:00 Clarify which files are “master executors” vs. “working copies” to reduce accidental edits.
Boundary Reminder: Seeds. No maintenance. No roadmap.