Chain-of-Thought
The model’s ultimate response is the prompt technique that allows you to reach by step by step by step.
The idea chain (chain-of-thought, CoT) is a prompt technique, which clearly encourages intermediate logical steps to reach the conclusion, rather than jump directly into the final cevaba of a language model. The app is usually very simple: it can be enough to add a phrase like "think step" to prompt or to show intermediate mind steps in sample solutions within prompt. This small change can significantly increase model accuracy in tasks that require mathematical problems, logic puzzles and multi-step reasoning; because the model divides a complex problem into smaller and manageable sub-step steps instead of solving in one big leap.
The reason why the thought chain is effective is the token of large language models, from left to right production: the intermediate steps clearly write, allowing the model to withstand the subsequent forecasts of the previous, which reduces the accumulation of errors. Technical creates a conceptual basis of new generation models, which are now called "mixing models" (reasoning models) and carries a long, internal thinking process before answering. Chain-of-thought is considered one of the most effective and low-cost techniques of prompt engineering.
The reason why the thought chain is effective is the token of large language models, from left to right production: the intermediate steps clearly write, allowing the model to withstand the subsequent forecasts of the previous, which reduces the accumulation of errors. Technical creates a conceptual basis of new generation models, which are now called "mixing models" (reasoning models) and carries a long, internal thinking process before answering. Chain-of-thought is considered one of the most effective and low-cost techniques of prompt engineering.