Getting the most out of an LLM
Lesson 1 / 8
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Lesson outcome
I can describe how to improve LLM output and use prompt engineering to improve LLM output.
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The performance of LLMs is highly dependent on how users craft their prompts.
Prompts should be specific and clear to enable the model to interpret what you want.
Prompts should provide relevant context or examples so the model can generate more accurate and tailored responses.
LLMs can reflect biases from their training data, so responses should be critically evaluated and cross-checked.
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This content is by Cashar and published under an open licence.