# Lessons Learned: Operational Guide for AI Model Selection

> **Generated:** 2026-05-11 09:00 PDT  
> **Based on:** 74 experiments (2026-04-26 to 2026-05-11)

_This is the actionable distillation. For evidence and methodology, see REPORT.md._

---

## Quick Reference: Model Selection by Task

```
┌─────────────────────────────────────────────────────────────────┐
│                    TASK TYPE DECISION TREE                      │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  Is this a VERIFICATION task?                                   │
│  (contradiction, consistency, race condition)                   │
│     │                                                           │
│     ├─ YES → Use GPT-5 + Opus (skip Sonnet)                    │
│     │        Sonnet has ~33% precision on verification          │
│     │                                                           │
│     └─ NO → Is this CROSS-DOCUMENT?                            │
│              │                                                  │
│              ├─ YES → Use Opus (2.4x faster, more findings)    │
│              │                                                  │
│              └─ NO → Is this HIGH-STAKES?                      │
│                       (financial, safety, regulatory)           │
│                       │                                         │
│                       ├─ YES → Run all three                   │
│                       │        (GPT-5 + Opus + Sonnet)         │
│                       │        Total: ~$1-2, worth it          │
│                       │                                         │
│                       └─ NO → Sonnet first-pass               │
│                               Add Opus if findings need depth   │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘
```

---

## Rules

### Rule 1: Match Model to Task Type

| If the task is... | Use this | Not this |
|-------------------|----------|----------|
| Finding what's missing | GPT-5 | Mini |
| Finding contradictions | Opus | Sonnet |
| Cross-document consistency | Opus | GPT-5 |
| Quick structural scan | Sonnet 4.6 | GPT-5 |
| Broad coverage (noise OK) | Sonnet 4.5 | Sonnet 4.6 |
| Adversarial attack paths | GPT-5 then Opus | Either alone |
| Regulatory compliance | GPT-5 | Opus |
| Operational blind spots | GPT-5 | Sonnet |

### Rule 2: Don't Trust Sonnet for Verification

Sonnet finds ~3 contradictions but only ~1 is genuine. The others are misreadings. Use Sonnet for *identification* tasks (what's here?), not *verification* tasks (is this true?).

### Rule 3: Isolate the Signal

When checking for something specific (bias, contradictions, missing assumptions), extract the relevant text and ask about it directly. Don't bury the question in a broad review mandate. Signal-to-noise ratio matters more than model capability.

### Rule 4: Run the Ensemble for High Stakes

For anything financial, safety-critical, or regulatory: run GPT-5 + Opus + Sonnet. Each finds things the others miss. The union is larger than any single model's output. Cost is trivial vs. the value.

### Rule 5: Give GPT-5 Enough Tokens

GPT-5 needs `max_completion_tokens` ≥ 16K. A truncated GPT-5 response is worse than a complete Opus response. Token budget matters more than model size.

### Rule 6: Break Large Outputs Into Sections

Single agents die generating 1000+ lines. Rich input is fine; it's output length that kills. For large generation tasks: break into sections, draft in parallel, assemble.

### Rule 7: Narrow Framing Doesn't Fix Reasoning Gaps

You cannot make Sonnet match GPT-5/Opus by writing a better prompt. Narrow framing changes WHAT it looks for, not HOW WELL it reasons. The gap is architectural, not prompt engineering.

---

## Operational Playbooks

### Playbook A: Architecture Document Review

1. **First pass (Sonnet 4.6):** ~30s, catches structural issues, broken refs, obvious gaps
2. **Deep analysis (GPT-5):** ~90s, finds domain-specific gaps, hidden assumptions, edge cases
3. **Design tensions (Opus):** ~60s, finds where the design contradicts itself
4. **Merge and dedupe:** Union of all three, remove duplicates, sort by severity

### Playbook B: Cross-Document Consistency Check

1. **Use Opus only.** It's 2.4x faster than GPT-5 and finds more issues.
2. **Provide both documents in a single prompt** (~25KB max)
3. **Explicitly exclude omissions** in the prompt — you want contradictions, not "Doc A covers X but Doc B doesn't"

### Playbook C: Adversarial Security Review

1. **First pass (GPT-5):** Exhaustive enumeration of attack surface
2. **Extension pass (Opus):** Give Opus GPT-5's findings, ask it to critique and extend
3. **Result:** 30% more findings at 28% more cost, with prioritization

### Playbook D: Regulatory Compliance Review

1. **First pass (Sonnet):** ~25s, identifies areas of concern
2. **Deep dive (GPT-5):** Regulatory specificity, correct citations, edge cases
3. **GPT-5's reasoning tokens are spent on verification** — trust its citations

### Playbook E: Contradiction Detection

1. **Use GPT-5 + Opus in parallel** (not Sonnet)
2. **GPT-5 finds:** Specification conflicts (same scenario, different prescriptions)
3. **Opus finds:** Logical impossibilities (rules that can't coexist)
4. **Neither dominates** — they find different classes of contradiction

---

## Anti-Patterns

### ❌ Anti-Pattern 1: Using Sonnet for Verification Tasks

**What happens:** Sonnet reports contradictions that aren't real. You waste time investigating false positives or worse, trust a false negative.

**Instead:** Use GPT-5 or Opus for any task requiring "is this true?" reasoning.

### ❌ Anti-Pattern 2: Giving GPT-5 a Broad Mandate

**What happens:** GPT-5 spawns sub-agents, times out, or dumps raw tool output instead of synthesizing.

**Instead:** Give GPT-5 explicit single-actor instructions + output format. For Claude, broader mandates are fine.

### ❌ Anti-Pattern 3: Burying Important Checks in Large Reviews

**What happens:** The model misses the important thing because it's one of 47 things to check.

**Instead:** Extract the important check and ask about it specifically. Signal-to-noise ratio matters.

### ❌ Anti-Pattern 4: Extrapolating Across Task Types

**What happens:** "GPT-5 was great at X, so I'll use it for Y" — and it's mediocre.

**Instead:** Task type predicts performance better than "model X is better." Check the task-type table.

### ❌ Anti-Pattern 5: Skipping the Union

**What happens:** You run one model, miss things another would have caught, and the bug reaches production.

**Instead:** For high-stakes work, run the ensemble. The cost is trivial vs. the risk.

### ❌ Anti-Pattern 6: Tuning Reasoning Effort

**What happens:** You spend time adjusting low/medium/high reasoning effort parameters.

**Instead:** Don't bother. It has negligible effect on analytical work. Task type is the lever.

### ❌ Anti-Pattern 7: Trying to Fix Sonnet with Prompts

**What happens:** You write increasingly narrow prompts trying to get Sonnet to match GPT-5's reasoning depth.

**Instead:** Accept that the gap is architectural. Use Sonnet for what it's good at (speed, breadth, structural review), use GPT-5/Opus for reasoning-heavy tasks.

---

## Model Personality Cheat Sheet

| Model | Personality | Default Behavior | Give It |
|-------|-------------|------------------|---------|
| **GPT-5** | Exhaustive enumerator | Lists everything systematically | Bounded tasks, explicit output format, single-actor instructions |
| **Opus** | Design critic | Finds tensions and contradictions | Open-ended analysis, room to reason about boundaries |
| **Sonnet 4.6** | Structural scanner | Fast, precise, shallow | Quick first-pass work, structural review |
| **Sonnet 4.5** | Broad coverage | More findings, more noise | When you want breadth over precision |
| **GPT-4.1** | Generic competent | Stays within document framing | Middle-ground cost-sensitive work |
| **GPT-4.1 Mini** | Template filler | Formulaic but catches obvious things | Bulk screening, sanity checks |

### Opus Superpower

Opus finds where the spec's **own assumptions are false**. It doesn't just find missing things — it finds things the spec *believes* to be true that *aren't*.

Examples:
- "Realized P&L cannot recover" — the de-escalation model assumes all metrics can improve, but this one fundamentally cannot (#31)
- "Forward detection logic is backwards" — spec describes triggers in the wrong direction (#32)
- "Stop-loss defeated by temporal composition" — safety mechanism rendered ineffective by slow strategy (#47)

### GPT-5 Superpower

GPT-5 reasons about the document's **relationship to the real world**. It asks "what must be true about the external world for this to work?"

Examples:
- Broker rate limiting (429s) bypasses "connection lost" detection (#9)
- Corporate actions bypass staleness detection (#9)
- DB "commit unknown outcome" causing restart loops (#9)
- Cross-symbol strategies with partial staleness (#9)
- IRS rule nuances that simplifications violate (#54)

---

## Decision Framework

### When to Add Another Model

| Situation | Action |
|-----------|--------|
| Sonnet found nothing | Add Opus (may find design tensions) |
| GPT-5 found lots but all similar | Add Opus (may find different class) |
| Opus found tensions but no enumeration | Add GPT-5 (exhaustive coverage) |
| Cross-document task | Use Opus only (2.4x faster) |
| Regulatory/compliance task | Use GPT-5 (correct citations) |

### When NOT to Add Another Model

| Situation | Action |
|-----------|--------|
| Quick structural scan | Sonnet alone is fine |
| Bulk screening | Mini alone is fine |
| Already ran GPT-5 + Opus | Adding Sonnet rarely helps |
| Low-stakes internal doc | One model is enough |

### Cost-Benefit Quick Calc

| Risk level | Model cost | Justified? |
|------------|------------|------------|
| Financial/safety | ~$1-2 for ensemble | Always yes |
| Customer-facing | ~$0.50 for GPT-5 | Usually yes |
| Internal process | ~$0.10 for Sonnet | Always yes |
| One-off exploration | ~$0.02 for Mini | Always yes |

---

## What We Still Don't Know

1. **Corpus bias:** All experiments used gargoyle docs. Rankings may differ for other domains.
2. **Run variance:** All findings are single-run. Stochastic variation is unquantified.
3. **Scale effects:** Largest doc tested is 1,110 lines. Unknown behavior at 2000+.
4. **Non-architecture domains:** These findings are for architecture document analysis, not coding, not chat, not creative writing.

---

## Summary: The Two Things That Matter Most

1. **Task type determines model choice.** Don't pick a model because "it's best." Pick the model that's best for THIS task type.

2. **The union beats any single model.** For high-stakes work, run the ensemble. Different models find qualitatively different things.

Everything else is optimization.