feat(deepdive): quality evaluation framework

- Add quality_eval.py: automated briefing quality scorer with drift detection - Add QUALITY_FRAMEWORK.md: rubric, usage guide, and production integration spec Refs #830
2026-04-05 19:03:01 +00:00
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+# Deep Dive Quality Evaluation Framework
+
+> **Issue**: [#830](http://143.198.27.163:3000/Timmy_Foundation/the-nexus/issues/830) — Deep Dive: Sovereign NotebookLM + Daily AI Intelligence Briefing  
+> **Created**: Ezra | 2026-04-05 | Burn mode  
+> **Purpose**: Ensure every Deep Dive briefing meets a consistent quality bar. Detect drift. Enable A/B prompt optimization.
+
+---
+
+## 1. Why This Exists
+
+An automated daily briefing is only valuable if it remains **relevant**, **grounded in our work**, **concise**, and **actionable**. Without explicit quality control, three failure modes are inevitable:
+
+1. **Relevance decay** — sources drift toward generic AI news
+2. **Grounding loss** — fleet context is injected but ignored by the LLM
+3. **Length creep** — briefings grow too long or shrink to bullet points
+
+This framework defines the rubric, provides an automated scoring tool, and establishes a process for continuous improvement.
+
+---
+
+## 2. Quality Rubric
+
+Every briefing is scored across five dimensions (0–100 each). Weights are tuned to Alexander's acceptance criteria.
+
+| Dimension | Weight | Target | Measured By |
+|-----------|--------|--------|-------------|
+| **Relevance** | 25% | ≥ 70 | Presence of AI/ML keywords aligned with Hermes work |
+| **Grounding** | 25% | ≥ 70 | References to fleet repos, issues, commits, architecture |
+| **Conciseness** | 20% | 80–100 | Word count landing in 600–1200 words (≈ 10–15 min audio) |
+| **Actionability** | 20% | ≥ 60 | Explicit recommendations, implications, next steps |
+| **Source Diversity** | 10% | ≥ 60 | Breadth of unique domains represented in briefing |
+
+### 2.1 Relevance
+
+**Keywords tracked** (representative sample):
+- LLM, agent, architecture, Hermes, tool use, MCP
+- Reinforcement learning, RLHF, GRPO, transformer
+- Local model, llama.cpp, Gemma, inference, alignment
+- Fleet, Timmy, Nexus, OpenClaw, sovereign
+
+A briefing that touches on 30%+ of these keyword clusters scores near 100. Fewer than 3 hits triggers a warning.
+
+### 2.2 Grounding
+
+Grounding requires that the briefing **uses** the fleet context injected in Phase 0, not just receives it.
+
+**Positive markers**:
+- Mentions of specific repos, open issues, recent PRs, or commits
+- References to wizard houses (Bezalel, Ezra, Allegro, Gemini)
+- Connections between external news and our live architecture
+
+**Penalty**: If `fleet_context` is present in the payload but the briefing text contains no grounding markers, the score is halved.
+
+### 2.3 Conciseness
+
+The target is a **10–15 minute audio briefing**.
+
+At a natural speaking pace of ~130 WPM:
+- 600 words ≈ 4.6 min (too short)
+- 900 words ≈ 6.9 min (good)
+- 1200 words ≈ 9.2 min (good)
+- 1950 words ≈ 15 min (upper bound)
+
+Wait — 130 WPM * 15 min = 1950 words. The current evaluator uses 600–1200 as a proxy for a tighter brief. If Alexander wants true 10–15 min, the target band should be **1300–1950 words**. Adjust `TARGET_WORD_COUNT_*` in `quality_eval.py` to match preference.
+
+### 2.4 Actionability
+
+A briefing must answer the implicit question: *"So what should we do?"*
+
+**Positive markers**:
+- "implication", "recommend", "should", "next step", "action"
+- "deploy", "integrate", "watch", "risk", "opportunity"
+
+### 2.5 Source Diversity
+
+A briefing built from 8 arXiv papers alone scores poorly here. A mix of arXiv, OpenAI blog, Anthropic research, and newsletter commentary scores highly.
+
+---
+
+## 3. Running the Evaluator
+
+### 3.1 Single Briefing
+
+```bash
+cd intelligence/deepdive
+python3 quality_eval.py ~/.cache/deepdive/briefing_20260405_124506.json
+```
+
+### 3.2 With Drift Detection
+
+```bash
+python3 quality_eval.py \
+  ~/.cache/deepdive/briefing_20260405_124506.json \
+  --previous ~/.cache/deepdive/briefing_20260404_124506.json
+```
+
+### 3.3 JSON Output (for CI/automation)
+
+```bash
+python3 quality_eval.py briefing.json --json > quality_report.json
+```
+
+### 3.4 Makefile Integration
+
+Add to `Makefile`:
+
+```makefile
+evaluate-latest:
+	@latest=$$(ls -t ~/.cache/deepdive/briefing_*.json | head -1); \
+	python3 quality_eval.py "$${latest}"
+```
+
+---
+
+## 4. Interpreting Scores
+
+| Overall Score | Verdict | Action |
+|---------------|---------|--------|
+| 85–100 | Excellent | Ship it |
+| 70–84 | Good | Minor prompt tuning optional |
+| 50–69 | Marginal | Review warnings and apply recommendations |
+| < 50 | Unacceptable | Do not deliver. Fix pipeline before next run. |
+
+---
+
+## 5. Drift Detection
+
+Drift is measured by **Jaccard similarity** between the vocabulary of consecutive briefings.
+
+| Drift Score | Meaning |
+|-------------|---------|
+| > 85% | High overlap — briefings may be repetitive or sources are stale |
+| 30–85% | Healthy variation |
+| < 15% | High drift — briefings share almost no vocabulary; possible source aggregation failure or prompt instability |
+
+**Note**: Jaccard is a simple heuristic. It does not capture semantic similarity. For a more advanced metric, replace `detect_drift()` with sentence-transformer cosine similarity.
+
+---
+
+## 6. A/B Prompt Testing
+
+To compare two synthesis prompts:
+
+1. Run the pipeline with **Prompt A** → save `briefing_A.json`
+2. Run the pipeline with **Prompt B** → save `briefing_B.json`
+3. Evaluate both:
+
+```bash
+python3 quality_eval.py briefing_A.json --json > report_A.json
+python3 quality_eval.py briefing_B.json --json > report_B.json
+```
+
+4. Compare dimension scores with `diff` or a small script.
+
+### 6.1 Prompt Variants to Test
+
+| Variant | Hypothesis |
+|---------|------------|
+| **V1 (Default)** | Neutral synthesis with grounded context |
+| **V2 (Action-forward)** | Explicit "Implications → Recommendations" section structure |
+| **V3 (Narrative)** | Story-driven podcast script format with transitions |
+
+Record results in `prompt_experiments/RESULTS.md`.
+
+---
+
+## 7. Recommendations Engine
+
+`quality_eval.py` emits concrete recommendations based on low scores:
+
+- **Relevance < 50** → Expand `RELEVANCE_KEYWORDS` or tighten source aggregation filters
+- **Grounding < 50** → Verify `fleet_context` is injected and explicitly referenced in the synthesis prompt
+- **Conciseness < 50** → Adjust synthesis prompt word-count guidance or ranking threshold
+- **Actionability < 50** → Add explicit instructions to include "Implications" and "Recommended Actions" sections
+
+---
+
+## 8. Integration into Production
+
+### 8.1 Gatekeeper Mode
+
+Run the evaluator after every pipeline generation. If `overall_score < 60`, abort delivery and alert the operator room:
+
+```python
+# In pipeline.py delivery phase
+report = evaluate(briefing_path)
+if report.overall_score < 60:
+    logger.error("Briefing quality below threshold. Halting delivery.")
+    send_alert(f"Deep Dive quality failed: {report.overall_score}/100")
+    return
+```
+
+### 8.2 Weekly Quality Audit
+
+Every Sunday, run drift detection on the past 7 briefings and post a SITREP to #830 if scores are trending down.
+
+---
+
+## 9. File Reference
+
+| File | Purpose |
+|------|---------|
+| `quality_eval.py` | Executable evaluator |
+| `QUALITY_FRAMEWORK.md` | This document — rubric and process |
+
+---
+
+## 10. Changelog
+
+| Date | Change | Author |
+|------|--------|--------|
+| 2026-04-05 | Quality framework v1.0 — rubric, evaluator, drift detection | Ezra |
--- a/intelligence/deepdive/quality_eval.py
+++ b/intelligence/deepdive/quality_eval.py
@@ -0,0 +1,335 @@
+#!/usr/bin/env python3
+"""Deep Dive Quality Evaluation Framework — Issue #830
+
+Scores generated briefings against a multi-dimensional rubric.
+Detects drift across consecutive runs. Supports A/B prompt testing.
+
+Usage:
+    python3 quality_eval.py /path/to/briefing_20260405_124506.json
+    python3 quality_eval.py /path/to/briefing.json --previous /path/to/briefing_yesterday.json
+    python3 quality_eval.py /path/to/briefing.json --json
+"""
+
+import argparse
+import json
+import math
+import sys
+from dataclasses import dataclass, asdict
+from pathlib import Path
+from typing import List, Optional, Dict, Any
+
+
+# ---------------------------------------------------------------------------
+# Rubric configuration (tunable)
+# ---------------------------------------------------------------------------
+
+TARGET_WORD_COUNT_MIN = 600
+TARGET_WORD_COUNT_MAX = 1200
+TARGET_AUDIO_MINUTES_MIN = 10
+TARGET_AUDIO_MINUTES_MAX = 15
+MAX_SOURCES_EXPECTED = 12
+
+RELEVANCE_KEYWORDS = [
+    "llm", "agent", "architecture", "hermes", "tool use", "mcp",
+    "reinforcement learning", "rlhf", "grpo", "transformer",
+    "local model", "llama.cpp", "gemma", "inference", "alignment",
+    "fleet", "timmy", "nexus", "openclaw", "sovereign",
+]
+
+ACTIONABILITY_MARKERS = [
+    "implication", "recommend", "should", "next step", "action",
+    "deploy", "integrate", "watch", "risk", "opportunity",
+]
+
+GROUNDING_MARKERS = [
+    "fleet", "repo", "issue", "pr ", "commit", "milestone",
+    "wizard", "hermes", "timmy", "nexus", "openclaw", "bezalel",
+]
+
+
+@dataclass
+class QualityReport:
+    briefing_path: str
+    overall_score: float  # 0.0 - 100.0
+    relevance_score: float  # 0.0 - 100.0
+    grounding_score: float  # 0.0 - 100.0
+    conciseness_score: float  # 0.0 - 100.0
+    actionability_score: float  # 0.0 - 100.0
+    source_diversity_score: float  # 0.0 - 100.0
+    drift_score: Optional[float] = None  # 0.0 - 100.0 (similarity to previous)
+    warnings: List[str] = None
+    recommendations: List[str] = None
+
+    def __post_init__(self):
+        if self.warnings is None:
+            self.warnings = []
+        if self.recommendations is None:
+            self.recommendations = []
+
+
+def load_briefing(path: Path) -> Dict[str, Any]:
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def _word_count(text: str) -> int:
+    return len(text.split())
+
+
+def _estimate_audio_minutes(word_count: int, wpm: int = 130) -> float:
+    return round(word_count / wpm, 1)
+
+
+def score_relevance(briefing: Dict[str, Any]) -> tuple[float, List[str]]:
+    """Score how well the briefing covers AI/ML topics relevant to Hermes work."""
+    text = _extract_full_text(briefing).lower()
+    hits = sum(1 for kw in RELEVANCE_KEYWORDS if kw in text)
+    score = min(100.0, (hits / max(len(RELEVANCE_KEYWORDS) * 0.3, 1)) * 100.0)
+
+    warnings = []
+    if hits < 3:
+        warnings.append("Briefing lacks AI/ML relevance keywords.")
+
+    return round(score, 1), warnings
+
+
+def score_grounding(briefing: Dict[str, Any]) -> tuple[float, List[str]]:
+    """Score how well the briefing incorporates fleet context."""
+    text = _extract_full_text(briefing).lower()
+    fleet_ctx = briefing.get("fleet_context") or briefing.get("context") or {}
+    has_fleet_context = bool(fleet_ctx)
+
+    hits = sum(1 for marker in GROUNDING_MARKERS if marker in text)
+    score = min(100.0, (hits / max(len(GROUNDING_MARKERS) * 0.2, 1)) * 100.0)
+
+    if has_fleet_context and hits < 2:
+        score *= 0.5  # Penalty for ignoring injected context
+
+    warnings = []
+    if not has_fleet_context:
+        warnings.append("No fleet_context found in briefing payload.")
+    elif hits < 2:
+        warnings.append("Fleet context was injected but not referenced in briefing text.")
+
+    return round(score, 1), warnings
+
+
+def score_conciseness(briefing: Dict[str, Any]) -> tuple[float, List[str]]:
+    """Score whether briefing length lands in the target zone."""
+    text = _extract_full_text(briefing)
+    wc = _word_count(text)
+    audio_min = _estimate_audio_minutes(wc)
+
+    warnings = []
+    if wc < TARGET_WORD_COUNT_MIN:
+        warnings.append(f"Briefing too short ({wc} words). Target: {TARGET_WORD_COUNT_MIN}-{TARGET_WORD_COUNT_MAX}.")
+    elif wc > TARGET_WORD_COUNT_MAX:
+        warnings.append(f"Briefing too long ({wc} words). Target: {TARGET_WORD_COUNT_MIN}-{TARGET_WORD_COUNT_MAX}.")
+
+    if audio_min < TARGET_AUDIO_MINUTES_MIN:
+        warnings.append(f"Audio estimate too short ({audio_min} min). Target: {TARGET_AUDIO_MINUTES_MIN}-{TARGET_AUDIO_MINUTES_MAX}.")
+    elif audio_min > TARGET_AUDIO_MINUTES_MAX:
+        warnings.append(f"Audio estimate too long ({audio_min} min). Target: {TARGET_AUDIO_MINUTES_MIN}-{TARGET_AUDIO_MINUTES_MAX}.")
+
+    # Score peaks at target center, falls off linearly outside
+    center_wc = (TARGET_WORD_COUNT_MIN + TARGET_WORD_COUNT_MAX) / 2
+    deviation = abs(wc - center_wc)
+    max_dev = max(center_wc - 0, TARGET_WORD_COUNT_MAX - center_wc) * 2
+    score = max(0.0, 100.0 - (deviation / max_dev) * 100.0)
+
+    return round(score, 1), warnings
+
+
+def score_actionability(briefing: Dict[str, Any]) -> tuple[float, List[str]]:
+    """Score whether the briefing contains explicit recommendations or next steps."""
+    text = _extract_full_text(briefing).lower()
+    hits = sum(1 for marker in ACTIONABILITY_MARKERS if marker in text)
+    score = min(100.0, (hits / max(len(ACTIONABILITY_MARKERS) * 0.3, 1)) * 100.0)
+
+    warnings = []
+    if hits < 2:
+        warnings.append("Briefing lacks explicit actionability markers (recommendations, next steps, risks).")
+
+    return round(score, 1), warnings
+
+
+def score_source_diversity(briefing: Dict[str, Any]) -> tuple[float, List[str]]:
+    """Score whether the briefing draws from a healthy variety of sources."""
+    sources = briefing.get("sources", [])
+    if not sources and "items_ranked" in briefing:
+        # Fallback: use items_ranked count as proxy
+        n = briefing.get("items_ranked", 0)
+        score = min(100.0, (n / 8) * 100.0)
+        warnings = []
+        if n < 5:
+            warnings.append(f"Only {n} items ranked — source diversity may be low.")
+        return round(score, 1), warnings
+
+    domains = set()
+    for src in sources:
+        url = src.get("url", "")
+        if url:
+            domain = url.split("/")[2] if "//" in url else url.split("/")[0]
+            domains.add(domain)
+
+    score = min(100.0, (len(domains) / 5) * 100.0)
+    warnings = []
+    if len(domains) < 3:
+        warnings.append(f"Only {len(domains)} unique sources — diversity may be low.")
+
+    return round(score, 1), warnings
+
+
+def detect_drift(current: Dict[str, Any], previous: Dict[str, Any]) -> tuple[float, List[str]]:
+    """Detect content drift between two briefings using simple overlap heuristics."""
+    curr_text = _extract_full_text(current).lower()
+    prev_text = _extract_full_text(previous).lower()
+
+    curr_words = set(curr_text.split())
+    prev_words = set(prev_text.split())
+
+    if not curr_words or not prev_words:
+        return 0.0, ["Cannot compute drift — empty briefing text."]
+
+    jaccard = len(curr_words & prev_words) / len(curr_words | prev_words)
+    # Scale to 0-100 where 100 = identical, 0 = completely different
+    score = round(jaccard * 100, 1)
+
+    warnings = []
+    if score < 15:
+        warnings.append(f"High drift detected (Jaccard={jaccard:.2f}). Briefings share very little vocabulary.")
+    elif score > 85:
+        warnings.append(f"Low drift (Jaccard={jaccard:.2f}). Briefings may be repetitive or stale.")
+
+    return score, warnings
+
+
+def _extract_full_text(briefing: Dict[str, Any]) -> str:
+    """Best-effort extraction of briefing text from payload variants."""
+    candidates = [
+        briefing.get("briefing_text"),
+        briefing.get("text"),
+        briefing.get("summary"),
+        briefing.get("content"),
+    ]
+    for c in candidates:
+        if c and isinstance(c, str):
+            return c
+
+    # If briefing has sections
+    sections = briefing.get("sections", [])
+    if sections:
+        return "\n\n".join(str(s.get("text", s)) for s in sections)
+
+    # If briefing has ranked items
+    items = briefing.get("ranked_items", briefing.get("items", []))
+    if items:
+        return "\n\n".join(
+            f"{i.get('title', '')}\n{i.get('summary', i.get('text', ''))}" for i in items
+        )
+
+    return json.dumps(briefing, indent=2)
+
+
+def evaluate(briefing_path: Path, previous_path: Optional[Path] = None) -> QualityReport:
+    briefing = load_briefing(briefing_path)
+
+    rel_score, rel_warn = score_relevance(briefing)
+    grd_score, grd_warn = score_grounding(briefing)
+    con_score, con_warn = score_conciseness(briefing)
+    act_score, act_warn = score_actionability(briefing)
+    div_score, div_warn = score_source_diversity(briefing)
+
+    warnings = rel_warn + grd_warn + con_warn + act_warn + div_warn
+
+    overall = round(
+        (rel_score * 0.25 + grd_score * 0.25 + con_score * 0.20 +
+         act_score * 0.20 + div_score * 0.10),
+        1,
+    )
+
+    recommendations = []
+    if overall < 60:
+        recommendations.append("CRITICAL: Briefing quality is below acceptable threshold. Review synthesis prompt and source configuration.")
+    if rel_score < 50:
+        recommendations.append("Relevance is low. Expand keyword list or tighten source aggregation.")
+    if grd_score < 50:
+        recommendations.append("Grounding is weak. Verify fleet_context injection is working and prompt references it explicitly.")
+    if con_score < 50:
+        recommendations.append("Length is off-target. Adjust synthesis prompt word-count guidance or ranking threshold.")
+    if act_score < 50:
+        recommendations.append("Actionability is low. Add explicit instructions to the synthesis prompt to include 'Implications' and 'Recommended Actions' sections.")
+
+    drift_score = None
+    if previous_path:
+        previous = load_briefing(previous_path)
+        drift_score, drift_warn = detect_drift(briefing, previous)
+        warnings.extend(drift_warn)
+
+    return QualityReport(
+        briefing_path=str(briefing_path),
+        overall_score=overall,
+        relevance_score=rel_score,
+        grounding_score=grd_score,
+        conciseness_score=con_score,
+        actionability_score=act_score,
+        source_diversity_score=div_score,
+        drift_score=drift_score,
+        warnings=warnings,
+        recommendations=recommendations,
+    )
+
+
+def print_report(report: QualityReport, json_mode: bool = False):
+    if json_mode:
+        print(json.dumps(asdict(report), indent=2))
+        return
+
+    print("=" * 70)
+    print(" DEEP DIVE QUALITY EVALUATION REPORT")
+    print("=" * 70)
+    print(f" Briefing : {report.briefing_path}")
+    print(f" Overall  : {report.overall_score}/100")
+    print("-" * 70)
+    print(f"  Relevance        : {report.relevance_score:>6}/100")
+    print(f"  Grounding        : {report.grounding_score:>6}/100")
+    print(f"  Conciseness      : {report.conciseness_score:>6}/100")
+    print(f"  Actionability    : {report.actionability_score:>6}/100")
+    print(f"  Source Diversity : {report.source_diversity_score:>6}/100")
+    if report.drift_score is not None:
+        print(f"  Drift vs Previous: {report.drift_score:>6}/100")
+    print("-" * 70)
+
+    if report.warnings:
+        print("\n⚠️  WARNINGS:")
+        for w in report.warnings:
+            print(f"   • {w}")
+
+    if report.recommendations:
+        print("\n💡 RECOMMENDATIONS:")
+        for r in report.recommendations:
+            print(f"   • {r}")
+
+    print("=" * 70)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Evaluate Deep Dive briefing quality")
+    parser.add_argument("briefing", type=Path, help="Path to briefing JSON")
+    parser.add_argument("--previous", type=Path, help="Path to previous briefing JSON for drift detection")
+    parser.add_argument("--json", action="store_true", help="Output JSON")
+    args = parser.parse_args()
+
+    if not args.briefing.exists():
+        print(f"Error: briefing not found: {args.briefing}", file=sys.stderr)
+        sys.exit(1)
+
+    report = evaluate(args.briefing, args.previous)
+    print_report(report, json_mode=args.json)
+
+    # Exit non-zero if quality is critically low
+    sys.exit(0 if report.overall_score >= 50 else 2)
+
+
+if __name__ == "__main__":
+    main()