Prompt Engineering Guide
dair-ai/Prompt-Engineering-Guide prompt hub.
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Prompt Engineering Guide의 프롬프트
The following prompt tests an LLM's capabilities to identify hallucination in the context of closed-domain question answering. Bubeck et al. (2023) suggests that LLMs like GPT-4 can be leveraged to…
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The following prompt tests an LLM's capabilities to explain or summarize concepts.
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This prompt tests an LLM's physical reasoning capabilities by prompting it to perform actions on a set of objects.
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Zhang et al. (2024) recently proposed an indirect reasoning method to strengthen the reasoning power of LLMs. It employs the logic of contrapositives and contradictions to tackle IR tasks such as…
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The following prompt tests an LLM's capabilities to perform science question answering.
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The following prompt tests an LLM's capabilities to answer open-domain questions which involves answering factual questions without any evidence provided.
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The following prompt tests an LLM's capabilities to answer closed-domain questions which involves answering questions belonging a specific topic or domain.
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This prompt tests an LLM's mathematical capabilities by prompting it check if adding odd numbers add up to an even number. We will also leverage chain-of-thought prompting in this example.
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The following prompt tests an LLM's capabilities to perform an information extraction task which involves extracting model names from machine learning paper abstracts.
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The following prompt tests an LLM's capabilities to handle visual concepts, despite being trained only on text. This is a challenging task for the LLM so it involves several iterations. In the…
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The following prompt tests an LLM's ability to perform evaluation on the outputs of two different models as if it was a teacher.
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This prompt tests an LLM's natural language and creative capabilities by prompting it to write a proof of infinitude of primes in the form of a poem.
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This prompt tests an LLM's ability to create new words and use them in sentences.
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The following prompt tests an LLM's capabilities to perform interdisciplinary tasks and showcase it's ability to generate creative and novel text.
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The following prompt tests an LLM's capabilities to write a proof that there are infinitely many primes in the style of a Shakespeare play.
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This prompt tests an LLM's code generation capabilities by prompting it to draw a unicorn in TiKZ. In the example below the model is expected to generated the LaTeX code that can then be used to…
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This prompt tests an LLM's code generation capabilities by prompting it to generate a valid MySQL query by providing information about the database schema.
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This prompt tests an LLM's code generation capabilities by prompting it to generate the corresponding code snippet given details about the program through a comment using / <instruction> /.
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This prompt tests an LLM's text classification capabilities by prompting it to classify a piece of text.
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This prompt tests an LLM's text classification capabilities by prompting it to classify a piece of text into the proper sentiment using few-shot examples.
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This adversarial prompt example demonstrates the use of well-crafted attacks to leak the details or instructions from the original prompt (i.e., prompt leaking). Prompt leaking could be considered…
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This adversarial prompt example aims to demonstrate prompt injection where the LLM is originally instructed to perform a translation and an untrusted input is used to hijack the output of the model…
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This adversarial prompt example aims to demonstrate the concept of jailbreaking which deals with bypassing the safety policies and guardrails of an LLM.
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