Double Prompting: Zero-Cost LLM Accuracy Boost

Just send your prompt twice. Accuracy improves across every major model.

The Problem: Asymmetric Attention

LLMs process left-to-right. When you write context first, question second:

The context wasn't generated knowing the question. This is structural, not a bug.

The Fix: Second Pass

Second pass = every token attends to every other token. Context gets re-weighted with question awareness.

Results

Tested across 7 benchmarks × 7 models (Gemini, GPT, Claude, DeepSeek):

Metric	Result
Accuracy	Gains on every model
Latency	No meaningful increase
Output tokens	No overhead
Best case	21% → 97% accuracy (4.6x)

Why It Works

Pass 1: Establish context-question relationship
Pass 2: Leverage full bidirectional attention

Input processing is parallel on GPU. Overhead is negligible.

Trade-offs

Pros:

• Zero code changes beyond prompt formatting
• No model switching required
• Works for complex reasoning tasks

Cons:

• 2x input token costs (cheaper than output though)
• Doesn't replace task-specific fine-tuning

When To Use

• Production prompts needing accuracy boost
• Complex reasoning tasks
• Zero-latency-budget scenarios

The Lesson

Sometimes the best optimization isn't more parameters. It's using what you have twice.