Targeted LLM Intervention Pattern

Context

You have a knowledge engineering pipeline that works well for most cases but struggles with a subset of ambiguous, complex, or edge cases. Human expert review is accurate but doesn’t scale. Comprehensive LLM use is expensive and often unnecessary.

Problem

How do you get LLM-quality judgment at scale without the cost of running every case through an LLM?

Solution

Design the traditional system to explicitly surface uncertainty. Use the LLM only for the uncertain subset where traditional methods are unreliable.

The architecture:

Input → Traditional System → Confident Cases → Output
                ↓
         Uncertain Cases → LLM Oracle → Validated Cases → Output

Key requirements:

1. Traditional system must quantify its own confidence
2. Uncertainty threshold must be tunable
3. LLM prompts should be binary validation questions, not open-ended generation
4. Natural language framing outperforms structured prompts

Consequences

Benefits:

• Cost scales with uncertainty, not volume
• LLM effort focused where it adds most value
• Traditional system’s strengths preserved
• Performance comparable to human experts (~20% error rate equivalent)

Tradeoffs:

• Requires traditional system to expose confidence scores
• Threshold tuning affects cost/quality balance
• LLM errors propagate only through uncertain cases

When to use:

• High-volume knowledge tasks with identifiable uncertainty
• Domain expertise is expensive or scarce
• Binary validation questions can replace open-ended judgment
• Cost efficiency matters more than perfection

When not to use:

• Traditional system can’t quantify uncertainty
• Tasks require creative generation, not validation
• Zero error tolerance (still need human-in-the-loop)

Related: 05-atom—llm-as-oracle-vs-aligner, 01-atom—human-in-the-loop