# SESSION TRACKER -- 2026-02-13 (Session 004)

## META
- session_id: 004
- date: 2026-02-13
- timestamp_utc: 2026-02-13T22:00:00Z
- phase: Pre-Phase 0 (Prompt Engineering Audit & Optimization)
- status: COMPLETE -- v3.1 produced after evidence-based audit
- previous_session: docs/tracker/2026-02-13_2115_session-003.md

## QUICK RESUME
**Read this file to pick up exactly where we left off.**

---

## WHAT WE ACCOMPLISHED THIS SESSION

### Expert Audit of v3 Prompt
Conducted world-class prompt engineering audit using 2 parallel research agents:

1. **Failure modes research** -- Lost in the Middle (Stanford arXiv 2307.03172), instruction density decay (arXiv 2411.07037), instruction hierarchy illusion (arXiv 2502.15851), context engineering paradigm (Anthropic Jun 2025), IntentGuard (ICLR 2026), structured output failures, DAG-specific failure modes (GraphTracer arXiv 2510.10581)
2. **Best practice research** -- Leaked system prompts from 30+ AI tools (Cursor, Claude Code, Devin, Windsurf), primacy/recency effects (ACL 2025), prompt compression techniques (LLMLingua, CompactPrompt), Anthropic system prompt guidance, schema-driven prompting

### 8 Evidence-Based Issues Found and Fixed

| Issue | Evidence | Fix Applied |
|-------|----------|-------------|
| Critical rules buried in middle | >30% accuracy drop mid-context (Stanford) | Moved to start (`<critical_rules>`) AND restated at end (`<critical_rules_restatement>`) |
| No primacy/recency reinforcement | ACL 2025: early+late items get most attention | Bookended: 8 rules at top, 6 restated at bottom |
| No subagent context scoping | Anthropic: subagents need "clear objective, format, boundary" | Added explicit protocol: pass ONLY phase objective + relevant inputs |
| No error recovery | AgentFlow/VIGIL: need classify→fix/retry/escalate | Added 3-tier recovery: Minor(inline) → Major(retry) → Critical(escalate). Max 2 retries. |
| No progress reporting | Anthropic: humans need visibility between gates | Added: report to human after every layer (8 reports, not just 2 checkpoints) |
| No few-shot examples | Anthropic: grounding improved accuracy 27%→98% | Added complete example KB document in Phase 3.1 |
| 122-line ASCII DAG wasting tokens | Anthropic: minimize preloaded context | Compressed to 8-line compact notation + legend |
| No semantic section markers | Anthropic: XML tags improve instruction adherence | Added XML-style tags: `<critical_rules>`, `<role>`, `<project>`, `<orchestrator_protocols>`, `<dag_structure>`, `<layers>`, `<constraints>`, `<output_structure>`, `<critical_rules_restatement>` |

### Version Progression
| Version | Lines | Key Architecture |
|---------|-------|-----------------|
| v1.0 | ~160 | 9 sequential phases, flat list |
| v2.0 | ~350 | 12 phases, dependency map |
| v3.0 | ~750 | 8-layer DAG, quality gates, parallel lanes |
| v3.1 | ~530 | Same DAG + primacy/recency, scoping, recovery, progress, examples, XML sections |

Note: v3.1 is ~220 lines SHORTER than v3.0 while adding 5 new capabilities. This is because the 122-line ASCII DAG was compressed and phase descriptions were made more concise (per instruction density research).

### Files Modified
| File | Change |
|------|--------|
| `docs/prompts/masterclass-project-kickoff.md` | v3.0 → v3.1: audit-driven optimization |
| `.cursor/rules/project-context.mdc` | Updated REUSABLE ASSETS for v3.1 |
| `docs/tracker/2026-02-13_2200_session-004.md` | This file (new) |

---

## ASSESSMENT: HAVE WE REACHED BEST POSSIBLE?

### What is now covered (grounded in evidence):
- DAG-based orchestration (OrchDAG, FLASH-SEARCHER)
- Parallel subagent execution (Anthropic multi-agent: 90% improvement)
- Quality gates with self-evaluation (Multi-Agent Reflexion)
- Human checkpoints at critical decision points
- Primacy/recency attention optimization (ACL 2025, Stanford)
- Lost-in-the-Middle mitigation (critical rules not in middle)
- Subagent context scoping (Anthropic guidance)
- Error recovery with escalation (VIGIL, AgentFlow)
- Progress reporting between gates
- Few-shot example grounding (Anthropic: 27%→98%)
- XML semantic sections (Anthropic system prompt guidance)
- Instruction density management (concise phases, specs in output docs)
- Source traceability (every backlog item traces to a requirement)
- Effort scaling (simple/medium/complex ideas)
- Comprehensive document taxonomy (40-70 docs)

### What remains as theoretical improvements (diminishing returns):
- **Constrained decoding**: Would enforce JSON output schemas via API -- not applicable since this is a paste-in prompt
- **Training-based instruction hierarchy**: Requires model fine-tuning -- not applicable
- **Prompt compression (LLMLingua)**: Could reduce tokens further but risks losing nuance
- **Per-model optimization**: Claude vs GPT vs Gemini behave differently; prompt is currently model-agnostic

### Verdict
The prompt is at the **practical ceiling** for a model-agnostic, paste-in orchestration protocol as of Feb 2026. Further improvements would require either (a) model-specific optimization, (b) API-level enforcement, or (c) a dedicated software tool rather than a prompt.

---

## WHAT IS NEEDED NEXT

### Immediate (Brendon Action Required)
1. **Set up Zoho Mail** -- Still pending. Follow `docs/guides/email-setup-steps.md`
2. **Test the v3.1 prompt** -- Use it on a new throwaway project idea to validate behavior

### Engineering (Next Session)
1. **Phase 0 execution** -- Initialize GitHub repo (SEC-011)
2. **CI/CD pipeline** -- GitHub Actions (SEC-012)
3. **Backlog migration** -- docs/backlog/ → GitHub Issues
