# Deep Dive Prompt Engineering — Knowledge Transfer

> **Issue**: [#830](https://forge.alexanderwhitestone.com/Timmy_Foundation/the-nexus/issues/830) — Deep Dive: Sovereign NotebookLM + Daily AI Intelligence Briefing  
> **Created**: 2026-04-05 by Ezra, Archivist  
> **Purpose**: Explain how the production synthesis prompt works, how to A/B test it, and how to maintain quality as the fleet evolves.

---

## 1. The Prompt Files

| File | Role | When to Change |
|------|------|----------------|
| `production_briefing_v1.txt` | Default prompt for daily briefing generation | When voice quality degrades or acceptance criteria drift |
| `production_briefing_v2_*.txt` | Experimental variants | During A/B tests |

---

## 2. Design Philosophy

The prompt is engineered around **three non-negotiables** from Alexander:

1. **Grounded in our world first** — Fleet context is not decoration. It must shape the narrative.
2. **Actionable, not encyclopedic** — Every headline needs a "so what" for Timmy Foundation work.
3. **Premium audio experience** — The output is a podcast script, not a report. Structure, pacing, and tone matter.

### Why 1,300–1,950 words?

At a natural speaking pace of ~130 WPM:
- 1,300 words ≈ 10 minutes
- 1,950 words ≈ 15 minutes

This hits the acceptance criterion for default audio runtime.

---

## 3. Prompt Architecture

The prompt has four layers:

### Layer 1: Persona
> "You are the voice of Deep Dive..."

This establishes tone, authority, and audience. It prevents the model from slipping into academic summarizer mode.

### Layer 2: Output Schema
> "Write this as a single continuous narrative... Structure the script in exactly these sections..."

The schema forces consistency. Without it, LLMs tend to produce bullet lists or inconsistent section ordering.

### Layer 3: Content Constraints
> "Every headline item MUST include a connection to our work..."

This is the grounding enforcement layer. It raises the cost of generic summaries.

### Layer 4: Dynamic Context
> `{{FLEET_CONTEXT}}` and `{{RESEARCH_ITEMS}}`

These are template variables substituted at runtime by `pipeline.py`. The prompt is **data-agnostic** — it defines how to think about whatever data is injected.

---

## 4. Integration with Pipeline

In `pipeline.py`, the `SynthesisEngine` loads the prompt file (if configured) and performs substitution:

```python
# Pseudo-code from pipeline.py
prompt_template = load_prompt("prompts/production_briefing_v1.txt")
prompt = prompt_template.replace("{{FLEET_CONTEXT}}", fleet_ctx.to_prompt_text())
prompt = prompt.replace("{{RESEARCH_ITEMS}}", format_items(items))
synthesis = self._call_llm(prompt)
```

To switch prompts, update `config.yaml`:

```yaml
synthesis:
  llm_endpoint: "http://localhost:4000/v1"
  prompt_file: "prompts/production_briefing_v1.txt"
  max_tokens: 2500
  temperature: 0.7
```

---

## 5. A/B Testing Protocol

### Hypothesis Template

| Variant | Hypothesis | Expected Change |
|---------|------------|-----------------|
| V1 (default) | Neutral podcast script with fleet grounding | Baseline |
| V2 (shorter) | Tighter 8–10 min briefings with sharper implications | Higher actionability score |
| V3 (narrative) | Story-driven opening with character arcs for projects | Higher engagement, risk of lower conciseness |

### Test Procedure

1. Copy `production_briefing_v1.txt` → `production_briefing_v2_test.txt`
2. Make a single controlled change (e.g., tighten word-count target, add explicit "Risk / Opportunity / Watch" subsection)
3. Run the pipeline with both prompts against the **same** set of research items:
   ```bash
   python3 pipeline.py --config config.v1.yaml --today --output briefing_v1.json
   python3 pipeline.py --config config.v2.yaml --today --output briefing_v2.json
   ```
4. Evaluate both with `quality_eval.py`:
   ```bash
   python3 quality_eval.py briefing_v1.json --json > report_v1.json
   python3 quality_eval.py briefing_v2.json --json > report_v2.json
   ```
5. Compare dimension scores. Winner becomes the new default.
6. Record results in `prompts/EXPERIMENTS.md`.

---

## 6. Common Failure Modes & Fixes

| Symptom | Root Cause | Fix |
|---------|------------|-----|
| Bullet lists instead of narrative | Model defaulting to summarization | Strengthen "single continuous narrative" instruction; add example opening |
| Generic connections ("this could be useful for AI") | Fleet context too abstract or model not penalized | Require explicit repo/issue names; verify `fleet_context` injection |
| Too short (< 1,000 words) | Model being overly efficient | Raise `max_tokens` to 2500+; tighten lower bound in prompt |
| Too long (> 2,200 words) | Model over-explaining each paper | Tighten upper bound; limit to top 4 items instead of 5 |
| Robotic tone | Temperature too low or persona too vague | Raise temperature to 0.75; strengthen voice rules |
| Ignores fleet context | Context injected at wrong position or too long | Move fleet context closer to the research items; truncate to top 3 repos/issues/commits |

---

## 7. Maintenance Checklist

Review this prompt monthly or whenever fleet structure changes significantly:

- [ ] Does the persona still match Alexander's preferred tone?
- [ ] Are the repo names in the examples still current?
- [ ] Does the word-count target still map to desired audio length?
- [ ] Have any new acceptance criteria emerged that need prompt constraints?
- [ ] Is the latest winning A/B variant promoted to `production_briefing_v1.txt`?

---

## 8. Accountability

| Role | Owner |
|------|-------|
| Prompt architecture | @ezra |
| A/B test execution | @gemini or assigned code agent |
| Quality evaluation | Automated via `quality_eval.py` |
| Final tone approval | @rockachopa (Alexander) |

---

*Last updated: 2026-04-05 by Ezra, Archivist*