Inductive Mode

Inductive Mode

Extract patterns and rules from multiple observations.

When to Use

• Multiple instances suggest a pattern
• Something keeps happening repeatedly
• Need to generalize from examples
• Question is "What pattern exists?"

Flow

Collection (≥5 instances) → Pattern Detection → Generalization → Confidence Bounds

Stage 1: Collection

Gather instances with consistent metadata before looking for patterns.

Minimum sample requirements:

Required for each instance:

• Unique identifier
• Timestamp
• Category/context
• Key attributes
• Outcome

Data quality checks:

• Completeness: What percentage of fields are filled?
• Consistency: Are categories and values standardized?
• Recency: How current is the data?

Challenge: "Are these instances comparable? Is there selection bias? What's missing?"

Example:

Collection: 24 content pieces, Jul-Dec 2024

ID	Type	Word Count	Channel	Conversions
C01	Case study	1,850	Organic	47
C02	How-to	2,200	Organic	32
C03	Case study	1,600	LinkedIn	28
...	...	...	...	...

Quality: Completeness 88%, Consistency 92%, Recency 95%

Gate: Must have ≥5 comparable instances before proceeding.

Stage 2: Pattern Detection

Identify patterns in the data.

Standard Pattern Types

System Architecture Patterns

For AI/software systems, also check:

Challenge: "Is this correlation or causation? What about the exception that breaks this?"

Scoring:

• Strong pattern: ≥80% of instances follow
• Moderate pattern: 60-79% follow
• Weak pattern: <60% follow (needs more data or is unreliable)

Example:

Patterns detected:

P1 (Frequency): Case studies convert at 2.3x average rate.

• 7/8 case studies above average (87.5%)
• Strength: Strong (0.82)

P2 (Correlation): Technical depth correlates with enterprise demos (r=0.68)

• Strength: Moderate
• Note: Correlation, not proven causation

P3 (Threshold): Posts >2,000 words perform 2.1x better on organic

• Clear step function at 2,000 word mark
• Strength: Strong (0.75)

P4 (Trend): LinkedIn engagement declining month-over-month

• Strength: Moderate (0.70)

Gate: At least one pattern must have strength ≥0.6 before proceeding.

Stage 3: Generalization

Form rules from validated patterns.

Rule Types

Required for each rule:

• Statement: Clear if-then formulation
• Derived from: Which patterns support it
• Mechanism: Why the rule works (hypothesis)
• Applicability: Where rule applies
• Exceptions: Known cases where rule doesn't hold

Challenge: "What's the boundary condition? Does this hold for [segment]? What are the exceptions?"

Example:

R1: Case studies convert at 2x+ rate vs other content types.

• Derived from: P1
• Mechanism: Social proof + specificity reduce purchase anxiety
• Applies to: Bottom-funnel, decision-stage content
• Exception: Narrow technical audiences (1/8 case studies underperformed)

R2: Target >2,000 words for SEO content.

• Derived from: P3
• Mechanism: Longer content ranks better, earns more backlinks
• Applies to: Organic-focused content
• Exception: News/announcement posts (brevity expected)

R3 (Tentative): Technical depth attracts enterprise buyers.

• Derived from: P2
• Mechanism: Signals expertise → builds trust
• Status: Correlation only—needs A/B test to confirm causation

Gate: Rules must have explicit applicability bounds and exceptions.

Stage 4: Confidence Bounds

Calculate reliability of rules.

Confidence formula:

Confidence = Base(N) × min(Strength, Consistency, Recency)

Base from sample size:

• N < 5: max 0.40
• N 5-10: max 0.60
• N 11-20: max 0.80
• N > 20: max 0.95

Exception rate:

• <10% exceptions: Rule is robust
• 10-20% exceptions: Rule has caveats
• 20-30% exceptions: Rule is situational

• 30% exceptions: Rule is unreliable—don't generalize

Challenge: "Is the sample large enough? Are we overconfident?"

Example:

R1 confidence calculation:

• Base (N=8): 0.60
• Strength: 0.82
• Consistency: 0.87
• Recency: 0.95
• Final: 0.60 × 0.82 = 0.49

Interpretation: Moderate confidence. Directionally useful but revisit when N≥15.

Validity period: Re-evaluate in 90 days or when N doubles.

Output Format

## Inductive Analysis: [Topic]

### Collection
- **Sample:** [N] instances, [date range]
- **Quality:** Completeness [%], Consistency [%]

### Patterns Detected

**P1: [Name]** (Strength: [X])
[Description with evidence]

**P2: [Name]** (Strength: [X])
[Description with evidence]

### Rules

**R1:** [If-then statement]
- Confidence: [%]
- Applies to: [Context]
- Exceptions: [Known exceptions]

### Uncertainty
- Sample size limitation: [What we'd see with more data]
- Exceptions unexplained: [Anomalies]
- Correlation vs causation: [What needs testing]

### Next Steps
1. [Action to validate or apply]
2. [Experiment to test mechanism]

Output Format

Pattern Analysis

## Pattern Analysis: [Topic]

### Collection
**Sample:** [N] instances, [date range]
**Source:** [Where data came from]
**Quality:** Completeness [%], Consistency [%]

### Patterns Detected

**P1: [Pattern Name]** (Strength: [0.0-1.0])
[Description with quantified evidence]

**P2: [Pattern Name]** (Strength: [0.0-1.0])
[Description with quantified evidence]

### Rules Derived

**R1:** [If-then statement]
- Confidence: [%]
- Applies to: [Context where rule holds]
- Exceptions: [Known cases where rule doesn't hold]
- Mechanism: [Why the rule works]

**R2:** [If-then statement]
- Confidence: [%]
- Applies to: [Context]
- Exceptions: [Known exceptions]

### Uncertainty
- **Sample size:** [Limitation and what more data would show]
- **Unexplained exceptions:** [Anomalies not yet understood]
- **Correlation vs causation:** [What needs testing]

### Next Steps
1. [Action to validate or apply]
2. [Experiment to test mechanism]

Assessment Output (for quality evaluation)

When evaluating against criteria:

## Assessment: [Target]

**Evaluator:** [Who]
**Date:** [When]

### Criteria

| Criterion | Weight | Pass Threshold |
|-----------|--------|----------------|
| [Name] | [0.0-1.0] | [What constitutes pass] |

### Findings

**[Criterion 1]:** [Pass/Partial/Fail]
- Score: [0.0-1.0]
- Evidence: [Specific observation]
- Gap: [If any, what's missing]

**[Criterion 2]:** [Pass/Partial/Fail]
- Score: [0.0-1.0]
- Evidence: [Specific observation]

### Overall

**Verdict:** [Pass / Conditional / Fail]
**Weighted Score:** [0.0-1.0]
**Confidence:** [0.0-1.0]

### Gaps

| Gap | Severity | Current | Required |
|-----|----------|---------|----------|
| [What's missing] | Blocking/Significant/Minor | [State] | [Target] |

*Note: Assessment describes quality. For recommendations, chain to recommendation output.*

Quality Gates

Anti-Patterns