Why Step-by-Step Prompting Works in AI
Understanding the Secrets to behind this prompt engineering superpower
“Think Step by Step” is one of the most popular techniques in AI. But do you understand why it works?
The research behind it is very interesting. reasoning doesn’t emerge from model size or compute power alone. Instead, it emerges from the STRUCTURE of training data.
The key finding: Step-by-step prompting works when training data exhibits LOCAL STRUCTURE — meaning observations tend to occur in overlapping neighborhoods of related concepts.
Think of it like this: the model learns to “hippity-hop” through connected pieces of information, chaining local inferences together to reach distant conclusions.
HOW THE RESEARCH WORKED
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Researchers used Bayesian networks to test three types of prediction:
1. Direct Prediction: Model immediately predicts the target from observations
2. Scaffolded Generation: Model generates intermediate variables in optimal order
3. Free Generation: Model spontaneously chooses which intermediate variables to generate
The experiments revealed something profound: models trained on locally-structured data could spontaneously generate useful reasoning paths. But models trained on data lacking proper locality structure failed — even when they had access to all the same information.
THE SURPRISING EFFICIENCY ADVANTAGE
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Here’s what shocked researchers:
- Fewer steps, better results: Local training produces FEWER intermediate variables, but they’re MORE RELEVANT
- Data efficiency wins: Models trained on local neighborhoods achieve better accuracy with LESS data than those trained on fully-observed global data
- Quality beats quantity: The structure of training data matters more than its size
This mirrors human expertise. Remember the chess master experiment? Masters could recreate real board positions from memory far better than novices. But when pieces were placed randomly (configurations that would never occur in real games), masters performed no better than beginners. Pattern recognition from familiar, locally-structured configurations is what enables expertise.
WHEN STEP-BY-STEP FAILS
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The research also revealed when chain-of-thought prompting doesn’t work:
When variables frequently co-occur but DON’T influence each other (wrong locality structure), models fall back to marginal probabilities. Without reliable local conditional relationships, there are no valid “steps” to chain together.
In other words: if the training data doesn’t encode proper local dependencies, no amount of clever prompting will create reasoning abilities.
Read the research here —
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