From 5f8381c2b71d65c93cd6d088b4c1c0e92948bfba Mon Sep 17 00:00:00 2001 From: Dan Erickson Date: Mon, 14 Apr 2025 18:00:01 -0700 Subject: [PATCH] Fix typo in third paragraph --- examples/gpt4-1_prompting_guide.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/gpt4-1_prompting_guide.ipynb b/examples/gpt4-1_prompting_guide.ipynb index e2b7cb73d9..5d95c6adc1 100644 --- a/examples/gpt4-1_prompting_guide.ipynb +++ b/examples/gpt4-1_prompting_guide.ipynb @@ -10,7 +10,7 @@ "\n", "Many typical best practices still apply to GPT-4.1, such as providing context examples, making instructions as specific and clear as possible, and inducing planning via prompting to maximize model intelligence. However, we expect that getting the most out of this model will require some prompt migration. GPT-4.1 is trained to follow instructions more closely and more literally than its predecessors, which tended to more liberally infer intent from user and system prompts. This also means, however, that GPT-4.1 is highly steerable and responsive to well-specified prompts - if model behavior is different from what you expect, a single sentence firmly and unequivocally clarifying your desired behavior is almost always sufficient to steer the model on course.\n", "\n", - "Please read on for prompt examples you can use as a reference, and remember that while this guidance is widely applicable, no advice is one-size-fits-all. AI engineering is inherently an empirical discipline, and large language models inherently nondeterministic; in addition to following this guide, we advise building informative evals and iterating often to ensure your prompt engineering changes are yielding benefits for your use case." + "Please read on for prompt examples you can use as a reference, and remember that while this guidance is widely applicable, no advice is one-size-fits-all. AI engineering is inherently an empirical discipline, and large language models are inherently nondeterministic; in addition to following this guide, we advise building informative evals and iterating often to ensure your prompt engineering changes are yielding benefits for your use case." ] }, {