In this co-hosted podcast episode, the speakers delve into the art of prompt engineering for large language models (LLMs), specifically focusing on its application for Kaggle competitions. They discuss configuring LLM outputs by adjusting parameters like output length, temperature, top K, and top P to control randomness and determinism. The hosts explore various prompting techniques, including zero-shot, one-shot, few-shot, system, role, contextual, step-back, chain of thought (CoT), tree of thoughts (ToT), and React, emphasizing their relevance for coding, debugging, and problem-solving in Kaggle. They also cover code prompting for tasks like code generation, explanation, translation, and debugging, and highlight best practices such as providing examples, designing for simplicity, using instructions over constraints, and documenting prompt attempts to improve results in Kaggle projects.
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