Prompt Engineering (PE) - Revision history

BPeat at 13:04, 16 March 2026

2026-03-16T13:04:23Z

BPeat at 13:03, 16 March 2026

2026-03-16T13:03:44Z

BPeat at 15:59, 16 January 2025

2025-01-16T15:59:02Z

BPeat at 00:20, 16 January 2025

2025-01-16T00:20:02Z

BPeat at 00:16, 16 January 2025

2025-01-16T00:16:54Z

BPeat at 00:14, 16 January 2025

2025-01-16T00:14:30Z

BPeat at 00:14, 16 January 2025

2025-01-16T00:14:05Z

BPeat at 17:13, 6 November 2024

2024-11-06T17:13:31Z

BPeat at 21:04, 28 April 2024

2024-04-28T21:04:35Z

BPeat: /* PromptLayer */

2024-04-04T08:27:09Z

‎PromptLayer

@@ Line 32: / Line 32: @@
 * [https://www.allabtai.com/prompt-engineering/ Prompt Engineering | Kris - All About AI ...Allabtai] ... and other informative AI videos
 * [https://www.promptingguide.ai/ Prompt Engineering Guide] ... [https://www.promptingguide.ai/techniques Techniques]
 * [https://writer.com/ai-content-detector/ Writer] ... AI Content Detector  ... grammar correction, brand voice, product terms enforcement, & text transformation

← Older revision		Revision as of 13:03, 16 March 2026
Line 44:		Line 44:


−	<b>~~Prompt~~</b> is the starting point for a model to generate output; a language model to generate text, providing [[context]] for text generation in natural-language-processing tasks such as chatbots and question-answering systems, or an application to generate images.	+	<b>[[Prompts]]</b> are the starting point for a model to generate output; a language model to generate text, providing [[context]] for text generation in natural-language-processing tasks such as chatbots and question-answering systems, or an application to generate images.

@@ Line 32: / Line 32: @@
 * [https://www.allabtai.com/prompt-engineering/ Prompt Engineering | Kris - All About AI ...Allabtai] ... and other informative AI videos
 * [https://www.promptingguide.ai/ Prompt Engineering Guide] ... [https://www.promptingguide.ai/techniques Techniques]
-* [https://docs.anthropic.com/en/prompt-library/library The Claude prompting library]
 * [https://writer.com/ai-content-detector/ Writer] ... AI Content Detector  ... grammar correction, brand voice, product terms enforcement, & text transformation
 * Lists:
-** [https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api Best practices for prompt engineering with OpenAI API] | [[OpenAI]]
+** [https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api Best practices for prompt engineering with OpenAI API] ... [https://platform.openai.com/docs/examples Examples] | [[OpenAI]]
 ** [https://github.com/dair-ai/Prompt-Engineering-Guide Prompt Engineering Guide | Elvis Saravia - dair.ai]  ... lecture, notebook, & slides
 ** [https://quickref.me/chatgpt [[ChatGPT]] cheatsheet | QuickRef.ME]
 ** [https://learnprompting.org/ Learn Prompting] ... a Free, Open Source Course on Communicating with Artificial Intelligence

@@ Line 32: / Line 32: @@
 * [https://www.allabtai.com/prompt-engineering/ Prompt Engineering | Kris - All About AI ...Allabtai] ... and other informative AI videos
 * [https://www.promptingguide.ai/ Prompt Engineering Guide] ... [https://www.promptingguide.ai/techniques Techniques]
 * [https://writer.com/ai-content-detector/ Writer] ... AI Content Detector  ... grammar correction, brand voice, product terms enforcement, & text transformation
 * Lists:

@@ Line 31: / Line 31: @@
 * [https://jobs.lever.co/Anthropic/e3cde481-d446-460f-b576-93cab67bd1ed Prompt Engineer | Anthropic]   ... Salary - The expected salary range for this position is $175k - $335k
 * [https://www.allabtai.com/prompt-engineering/ Prompt Engineering | Kris - All About AI ...Allabtai] ... and other informative AI videos
-* [https://www.promptingguide.ai/ Prompt Engineering Guide]
+* [https://www.promptingguide.ai/ Prompt Engineering Guide] ... [https://www.promptingguide.ai/techniques Techniques]
 * [https://writer.com/ai-content-detector/ Writer] ... AI Content Detector  ... grammar correction, brand voice, product terms enforcement, & text transformation
 * Lists:

@@ Line 21: / Line 21: @@
 * [[Prompt Engineering (PE)]] ...[[Prompt Engineering (PE)#PromptBase|PromptBase]] ... [[Prompt Injection Attack]]
 * [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 * [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[JavaScript]] ... [[Generative AI with JavaScript|GenAI w/ JavaScript]] ... [[TensorFlow]] ... [[PyTorch]]

@@ Line 23: / Line 23: @@
 * [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 * [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[JavaScript]] ... [[Generative AI with JavaScript|GenAI w/ JavaScript]] ... [[TensorFlow]] ... [[PyTorch]]
-* [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]]
+* [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] ... [[Game Design | Design]]
 * [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
 * [[Attention]] Mechanism  ...[[Transformer]] Model   ...[[Generative Pre-trained Transformer (GPT)]]

@@ Line 209: / Line 209: @@
 * Idea generator: provide idea on <u>concept</u>
 * Language Tutor: assist with language learning by explaining grammar rules, providing vocabulary suggestions, and offering practice exercises.
-* Problem Solver: assist in finding solutions to complex problems by offering different perspectives and brainstorming potential approaches.
+* Problem Solver: assist in finding solutions to complex problems by offering different [[perspective]]s and brainstorming potential approaches.
 * Research Assistant: help gather information, conduct online research, and present organized findings on a given topic.
 * Teacher: provide an answer on a grade level; e.g.7th grade
@@ Line 442: / Line 442: @@
 After I have a rough idea of what I want to accomplish, I try to narrow things down to people, places and things - the core actors or main drivers in the scene I’m trying to construct. I use the service to generate a few rough prompts to get a feel for what the scene might look like. I find it much easier to take something that works well and then add on to it rather than having to go back and remove things until it looks better. You start with the big important strokes and then work in the finer details.
-...  I see prompt writing from the perspective of an artist, coder and engineer. I use my programming experience to help me understand how the service may interpret my prompt, which guides me to more effective tinkering with it to coax the results I’m after. Every word in a prompt has a weight associated with it, so trying to work out what works best and where becomes a core asset in the skillset. My background in software quality assurance is a pretty big driver in that “what happens if” style of thinking. - [https://www.theverge.com/2022/9/2/23326868/dalle-midjourney-ai-promptbase-prompt-market-sales-artist-interview Professional AI whisperers have launched a marketplace for DALL-E prompts | Justin Reckling interviewed by article author Adi Robertson - The Verge]
+...  I see prompt writing from the [[perspective]] of an artist, coder and engineer. I use my programming experience to help me understand how the service may interpret my prompt, which guides me to more effective tinkering with it to coax the results I’m after. Every word in a prompt has a weight associated with it, so trying to work out what works best and where becomes a core asset in the skillset. My background in software quality assurance is a pretty big driver in that “what happens if” style of thinking. - [https://www.theverge.com/2022/9/2/23326868/dalle-midjourney-ai-promptbase-prompt-market-sales-artist-interview Professional AI whisperers have launched a marketplace for DALL-E prompts | Justin Reckling interviewed by article author Adi Robertson - The Verge]
 = Generative AI Pattern Language =
@@ Line 468: / Line 468: @@
 * a dragon and a knight
-These prompts can help you write more specific and relevant content, or explore different angles and perspectives.
+These prompts can help you write more specific and relevant content, or explore different angles and [[perspective]]s.
@@ Line 486: / Line 486: @@
 ** Singleton pattern: This pattern ensures that only one instance of a class exists and provides a global point of access to it. It can be used to create prompts that have a unique or specific identity or purpose, such as “the only photo of Albert Einstein smiling”.
-* <b>Transformational</b>: used to transform data from one form to another; a type of design pattern that deal with object behavior mechanisms, aiming to change the state or structure of objects in a flexible and dynamic way. In generative AI prompting, transformational patterns can be used to modify the output of the model, such as the tone, perspective, format, or content. For example, some transformational patterns that can be used in generative AI prompting are:
+* <b>Transformational</b>: used to transform data from one form to another; a type of design pattern that deal with object behavior mechanisms, aiming to change the state or structure of objects in a flexible and dynamic way. In generative AI prompting, transformational patterns can be used to modify the output of the model, such as the tone, [[perspective]], format, or content. For example, some transformational patterns that can be used in generative AI prompting are:
 ** Adapter pattern: This pattern allows adapting an existing interface to another interface that is expected by the client. It can be used to create prompts that change the format or style of the output, such as “Rewrite this sentence in passive voice: She opened the door.”
 ** Decorator pattern: This pattern allows adding new functionality to an existing object without altering its structure. It can be used to create prompts that add more details or information to the output, such as “Add a catchy slogan to this product description: A smartwatch that tracks your fitness and health.”

@@ Line 396: / Line 396: @@
 * [https://docs.promptlayer.com/introduction PromptLayer] ... is the largest platform for prompt engineering
-PromptLayer is a platform designed specifically for those who work with large language models (LLMs) and the process of creating instructions, or prompts, to get the desired results from these models. This process is called prompt engineering.
+PromptLayer is a platform designed specifically for those who work with large language models (LLMs) and the process of creating instructions, or prompts, to get the desired results from these models. PromptLayer sits in between your code and the LLM's API, acting as a <u>middleware layer</u>. Imagine you have code that interacts with an LLM, like OpenAI's API. PromptLayer acts like an extension to your existing code. It captures the prompts you send to the LLM and stores additional information about those interactions. Here's a breakdown:
 Here are some of the benefits PromptLayer offers:
 * Track and Manage Prompts: PromptLayer helps you keep track of all the different prompts you create and use. This can be helpful for staying organized and revisiting successful prompts in the future.
-LLM Observability: It provides tools to see how your prompts are being used by the LLM. This can help you identify any issues and improve your prompts over time.
+* LLM Observability: It provides tools to see how your prompts are being used by the LLM. This can help you identify any issues and improve your prompts over time.
-Collaboration: If you're working with a team on LLM projects, PromptLayer can facilitate collaboration by making it easier to share and discuss prompts.
+* Collaboration: If you're working with a team on LLM projects, PromptLayer can facilitate collaboration by making it easier to share and discuss prompts.
-Cost Tracking: If you're using a paid LLM service, PromptLayer can help you track your usage costs.
+* Cost Tracking: If you're using a paid LLM service, PromptLayer can help you track your usage costs.
 <youtube>B5usn2_XQVE</youtube>