Skip to content

Commit 38dbc46

Browse files
committed
update
1 parent b185261 commit 38dbc46

File tree

3 files changed

+64
-42
lines changed

3 files changed

+64
-42
lines changed

articles/cognitive-services/openai/concepts/advanced-prompt-engineering.md

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -6,26 +6,26 @@ author: mrbullwinkle
66
ms.author: mbullwin
77
ms.service: cognitive-services
88
ms.topic: conceptual
9-
ms.date: 03/21/2023
9+
ms.date: 04/07/2023
1010
manager: nitinme
1111
keywords: ChatGPT
1212
zone_pivot_groups: openai-prompt
1313
---
1414

1515
# Prompt Engineering Techniques
1616

17-
This guide will walk you through some advanced techniques in prompt design and prompt engineering. If you are new to prompt engineering we recommend starting with our [introduction to prompt engineering guide](prompt-engineering.md).
17+
This guide will walk you through some advanced techniques in prompt design and prompt engineering. If you're new to prompt engineering, we recommend starting with our [introduction to prompt engineering guide](prompt-engineering.md).
1818

1919
While the principles of prompt engineering can be generalized across many different model types, certain models expect a specialized prompt structure. For Azure OpenAI GPT models, there are currently two distinct APIs where prompt engineering comes into play:
2020

2121
- Chat Completion API.
2222
- Completion API.
2323

24-
Each API requires input data to be formatted differently, which in turn impacts overall prompt design. The Chat Completion API supports the ChatGPT (preview) and GPT-4 (preview) models. These models are designed to take input formatted in a [specific chat-like transcript](../how-to/chatgpt.md) divided across an array of dictionaries.
24+
Each API requires input data to be formatted differently, which in turn impacts overall prompt design. The Chat Completion API supports the ChatGPT (preview) and GPT-4 (preview) models. These models are designed to take input formatted in a [specific chat-like transcript](../how-to/chatgpt.md) stored inside an array of dictionaries.
2525

26-
The Completion API supports the older GPT-3 models and has much more flexible input requirements in that it takes a string of text with no specific format rules. Technically the ChatGPT (preview) models can be used with either API's, but we strongly recommend using the Chat Completion API for these models. To learn more, please consult our [in-depth guide on using the two API](../how-to/chatgpt.md).
26+
The Completion API supports the older GPT-3 models and has much more flexible input requirements in that it takes a string of text with no specific format rules. Technically the ChatGPT (preview) models can be used with either APIs, but we strongly recommend using the Chat Completion API for these models. To learn more, please consult our [in-depth guide on using these APIs](../how-to/chatgpt.md).
2727

28-
The techniques in this guide will teach you strategies for increasing the accuracy and grounding of responses you generate with a Large Language Model (LLM). It is, however, important to remember that even when using prompt engineering effectively you still need to validate the responses the models generate. Just because a carefully crafted prompt worked well for a particular scenario doesn't necessarily mean it will generalize more broadly to certain use cases. Understanding the [limitations of LLM's](/legal/cognitive-services/openai/transparency-note?context=%2Fazure%2Fcognitive-services%2Fopenai%2Fcontext%2Fcontext#limitations), is just as important as understanding how to leverage their strengths.
28+
The techniques in this guide will teach you strategies for increasing the accuracy and grounding of responses you generate with a Large Language Model (LLM). It is, however, important to remember that even when using prompt engineering effectively you still need to validate the responses the models generate. Just because a carefully crafted prompt worked well for a particular scenario doesn't necessarily mean it will generalize more broadly to certain use cases. Understanding the [limitations of LLMs](/legal/cognitive-services/openai/transparency-note?context=%2Fazure%2Fcognitive-services%2Fopenai%2Fcontext%2Fcontext#limitations), is just as important as understanding how to leverage their strengths.
2929

3030
::: zone pivot="programming-language-chat-completions"
3131

0 commit comments

Comments
 (0)