In this tutorial, we’ll explore how to implement Chain-of-Thought (CoT) reasoning using the Mirascope library and Groq’s LLaMA 3 model. Rather than having the model jump straight to an answer, CoT reasoning encourages it to break the problem down into logical steps—much like how a human would solve it. This approach improves accuracy, transparency, and helps tackle complex, multi-step tasks more reliably. We’ll guide you through setting up the schema, defining step-by-step reasoning calls, generating final answers, and visualizing the thinking process in a structured way. We’ll be asking the LLM a relative velocity question – “If a train leaves…
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