Campaign Overview
The Grace quality refinement case study is a quantified self-improvement narrative: unit quality from 0.305 (critical) to 0.853 (passing) in a single H5→H8→H3 cycle. The story has technical credibility (conformance 0.45→0.85, completeness 0.20→0.88, efficiency held), human gates (H7 manual review), and safety measures (dry-run, regression detection, rollback). This lands for technical audiences (LLM practitioners, prompt engineers, ops) and product audiences (quality assurance, automation ROI).Content Angles
Case Study: “Systematic Unit Improvement”
Register: Protocol-native (HN Show HN, r/MachineLearning, dev blogs) Angle: Walk through the domain-map unit: what was broken (null result field, missing schema_violations), how H5 identified it (confidence-ranked suggestions), how H8 fixed it (prompt enhancement + schema fallback), how H3 validated it (score jump from 0.305 → 0.853). Hook: “A prompt unit scored 0.305. Here’s how I identified the root cause, fixed it, and validated the improvement — all automated.”ROI Angle: “179% Quality Improvement”
Register: Enterprise/SaaS (LinkedIn, dev.to, Substack) Angle: From a business lens: the refinement loop is capital-efficient (bash scripts, no LLM API calls for H5 analysis, just git-backed unit improvements). 179% score improvement translates to better conformance, completeness, reliability. Hook: “Automated refinement loops don’t have to cost money. This one runs on git + bash + YAML.”Prompt Tuning Methodology: “Confidence-Ranked Suggestions”
Register: Protocol-native (OpenAI forums, prompt eng communities, stratt.engineer) Angle: H5’s confidence scoring model (signal frequency, domain precedent, risk assessment, example basis) is a reusable methodology for any prompt refinement project. Share the 0.92 confidence suggestion vs. 0.78 low-confidence one — why one was safe to auto-execute, the other needed review. Hook: “Not all prompt suggestions are equal. Here’s how to rank them by confidence and execute safely.”Campaign Trail Alignment
- CT-05 PROTOCOL_NATIVE: Quality metrics + closed-loop refinement as a deep technical thread
- CT-04 SOL_LOG: Weekly status: “This week’s automation cycle improved 3 units”
- CT-02 AVIATION_BRIDGE (aspirational): “I verify critical software. Here’s how I verify AI improvements too.”
Distribution Plan (14-day cycle)
| Day | Platform | Content | Register | Link |
|---|---|---|---|---|
| 1 | Blog | Full case study: domain-map 0.305→0.853 | Case Study | Yes |
| 2 | HN | Show HN: Automated unit refinement with regression detection | Case Study | No |
| 3 | Bluesky + X | ”Domain-map scored 0.305. Dry-run showed it was fixable. Now it’s 0.853.” | Metrics angle | No |
| 5 | ”Automated refinement is ROI-positive. Here’s the math.” | Enterprise | Yes | |
| 7 | r/MachineLearning | ”Confidence-ranked prompt suggestions: a methodology” | Protocol-native | No |
| 10 | stratt.engineer | ”Quality scoring heuristic: 40% conformance, 35% completeness, 25% efficiency” | Protocol-native | Yes |
| 12 | dev.to | ”Bash pipelines for automated prompt tuning” | Protocol-native | No |
| 14 | Bluesky + X | ”A week of automation: 3 units improved, zero regressions, all git-backed” | Founder workflow | No |