Teaching AI models to reason step by step for dramatically better accuracy on complex tasks.
Catalyst Team
Research
A landmark 2022 Google Brain paper demonstrated something surprising: simply adding 'Let's think step by step' to a prompt dramatically improved model performance on multi-step reasoning tasks. Chain-of-thought (CoT) prompting has since become one of the most reliable techniques for improving output quality on complex problems.
Language models predict the next token based on the preceding context. When a model is forced to generate intermediate reasoning steps, those steps become part of the context for subsequent tokens — effectively giving the model more 'working memory' to solve the problem with. It's analogous to how humans perform better on complex problems when they write out intermediate steps rather than trying to calculate the answer in their heads.
The simplest form is zero-shot CoT — you don't provide any examples, you just instruct the model to reason aloud.
# Without CoT
Q: A store has 144 items. 30% are sold on Monday, then 25% of the
remaining are sold on Tuesday. How many remain?
# With Zero-Shot CoT
Q: A store has 144 items. 30% are sold on Monday, then 25% of the
remaining are sold on Tuesday. How many remain?
Think through this step by step before giving your final answer.Research consistently shows that CoT provides the largest gains on tasks requiring more than 3-4 reasoning steps. For simple, direct queries, the overhead is unnecessary.
Providing 2-3 worked examples of the reasoning process you want the model to follow is the most powerful variant. By showing the exact format and depth of reasoning you expect, the model has an explicit template to follow. This is especially valuable when you need consistent output structure across many queries.
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