Chain of Thought is an
AI model capability to solve problems step by step, showing its
reasoning sequentially. It can be activated through
prompting techniques or integrated directly into the model as a native function.
This capability works by breaking down complex problems into simpler intermediate steps, similar to how a student shows their work on an exam. For example, applying CoT, when facing a math problem, instead of giving just the final result, they show: "First I multiply 8 x 3 = 24, then I add 15, getting 24 + 15 = 39". This methodology is especially effective in logical
reasoning, mathematics, complex text analysis, and multi-step decision making.
Models can implement CoT in various ways: through
prompting techniques (explicitly asking it to show
reasoning) or as an integrated function that activates manually or automatically in complex tasks.
CoT is mainly used in
large language models and its impact is notable: it improves performance by up to 50% or more in
reasoning tasks. This capability has revolutionized AI, making responses more transparent, verifiable and reliable, allowing errors to be identified in the
reasoning process.