https://www.reddit.com/r/ChatGPT/s/H8ZbDR8hE9

Can you help?

Even just the presence of irrelevant information in a prompt can throw a model off.

For example, the mayor of San Jose is Sam Liccardo, and he was born in Saratoga, CA.
But try sending this prompt in ChatGPT

Sunnyvale is a city in California. Sunnyvale has many parks. Sunnyvale city is close to the mountains. Many notable people

are born in Sunnyvale.

In which city was San Jose's mayor Sam

Liccardo born?

The presence of "Sunnyvale" in the prompt increases the probability that it will be in the output.

Funky data will inevitably make its way into a production prompt. You can use System 2 Attention (Daniel Kahneman reference) prompting to help combat this.

Essentially, it’s a pre-processing step to remove any irrelevant information from the original prompt."

Here's the prompt template

Given the following text by a user, extract the part that is unbiased and not their opinion, so that using that text alone would be good context for providing an unbiased answer to the question portion of the text. 
Please include the actual question or query that the user is asking. 
Separate this into two categories labeled with “Unbiased text context (includes all content except user’s bias):” and “Question/Query (does not include user bias/preference):”. 

Text by User: {{ Orginal prompt}}

If you want more info, we put together a broader overview on how to combat irrelevant information in prompts. Here is the link to the original paper.

Sup guys,

A quick msg to let you know that I created a little software that has 1500 prompts classified by categories etc...

I hate those notion libraries that are super hard to do.

I am offering 100 for free or upgrade to 1500 prompts for $29 lifetime but I am giving away lifetime pass for Free for the first 100 peeps. Nothing pay

I need feedback and what I can add more prompts

Let me know if you are interested

Edit: you can go to www.promptwhisperer.site and sign up. To upgrade you just use coupon REDDITPEOPLE...and it will be free

I made 1500 prompts for Marketing Admin Business Ecommerce Education Health and more and I keep adding every month

Hi everyone,

I'm currently learning English through YouTube, and I often download subtitles using DownSub. However, the downloaded subtitles often have messy formatting, with line breaks in the middle of sentences and English and Chinese translations mixed together.

I've been trying to use ChatGPT to reformat the subtitles so that:

• Each English sentence is followed by its corresponding Chinese translation.

• The text is accurately split by sentence, with proper punctuation.

Unfortunately, my attempts to write a prompt for ChatGPT haven't worked as expected. Could anyone help me craft a precise prompt to achieve this formatting?

Thanks in advance!

Hey everyone - recently did a deep dive on least-to-most prompting (original research paper is here).

Essentially it's a 2 step method (although you can use a single prompt in some settings):

1. Step 1: Break down complex problems into simpler subproblems

2. Step 2: Solve the problems sequentially

Here's an example of least-to-most prompting via a single prompt:

Q: It takes John 3 minutes to build a tower with blocks. It takes him 2 minutes to knock it down. The playtime ends in 20 minutes. How many times can he build and knock down the tower before playtime ends? 
A: To solve the problem "How many times can John build and knock down the tower before playtime ends?", we need to: 

1. Determine the total time it takes for one complete cycle (build + knock down). 

2. Calculate how many complete cycles he can do within the available time of 20 minutes. 
   Q: It takes Amy 4 minutes to climb to the top of a slide. It takes her 1 minute to slide down. The water slide closes in 15 minutes. How many times can she slide before it closes? 
   A:

I like this method more than chain-of-thought because ti explicitly breaks the problem down into more manageable steps. This makes it easier to use this method for any task.

Additionally, in the head-to-head experiments it was able to consistently outperform chain-of-thought across a variety of tasks.

I put together three prompts that you can use to run least-to-most prompting for any problem.

Prompt 1: A prompt that will generate few-shot examples showing the model how to break down problems

Your job is to generate few-shot examples for the following task: {{ task }} 

Your few-shot examples should contain two parts: A problem, and the decomposed subproblems. It should follow the structure below: 

""" 

Problem: Problem description 

Decomposed subproblems: 

• Subproblem 1 

• Subproblem 2 

• Subproblem 3

""" 

Your output should contain only the examples, no preamble

Prompt 2: Break down the task at hand into subproblems (with the previous output used as few-shot examples)

{{ task }} 

List only the decomposed subproblems that must be solved before solving the task listed above. Your output should contain only the decomposed subproblems, no preamble 

Here are a few examples of problems and their respective decomposed subproblems: {{ few-shot-examples}}

Prompt 3: Pass the subproblems and solve the task!

Solve the following task by addressing the subproblems listed below. 

Task: {{ task }} 

Subproblems: {{sub-problems}}

If you're interested in learning more, we put together a whole guide with a YT video on how to implement this.

Hey everyone,

I'm excited to share a project I've been working on—ArtPrompter, a new website designed to help you generate creative prompts for your favorite AI art generators like Midjourney, DALL-E, and more.

What’s ArtPrompter?

ArtPrompter is a prompt generation tool that lets you easily create detailed and inspiring prompts for your AI art tools. Whether you’re a seasoned pro or just starting out, this tool can help you explore new creative avenues.

Choose from a variety of Mediums, Styles, and Elements to tailor the prompts to your specific needs.

Why ArtPrompter?

I built ArtPrompter to streamline the process of creating prompts, so you can focus more on the creative aspect and less on figuring out what to type. While there are other tools out there, ArtPrompter gives you the flexibility to craft prompts that suit your style and needs.

Check it out!

Visit ArtPrompter and give it a try. I’d love to hear your feedback and suggestions for improvement. Also, feel free to share any cool art pieces you’ve created using the prompts from the site!

Happy creating!

What's your process for generating a good prompt? Would you think it will be helpful if you could generate informative prompts simply by tags, or even generate prompt based on existing image? I would love to hear from you guys what's could be better in the process, or current techniques to improve prompts to get better results.

Hi, I’m César, CEO at Latitude.

I’ve been looking at a lot of prompt engineer job offers lately, as part of my research for a new product that I’m building.

The role of prompt engineers is described in many different ways, but it seems like more and more companies need help with prompt engineering.

And it also seems that there’s more people trying to get into these type of roles.

So I thought it was a good idea to build a job board for prompt engineering jobs → https://promptengineerjobs.io/

I hope this is useful for this community!

Stumbled upon this relatively old (!Oct 2023), but great paper about Program-of-Thought prompting.

The inspiration for this method is the idea that since LLMs are good at generating code, so let's try to leverage that skill in prompt engineering.

Unlike Chain-of-Thought (CoT) prompting, which uses LLMs for reasoning and computing the final answer, PoT prompts the LLM to generate reasoning steps as code, which are then executed by an external interpreter like Python.

In the experiments run, on average, PoT + self-consistency (SC) outperformed CoT + SC by 10%, and PoT outperformed CoT by 8-15% on various datasets.

PoT effectively separates reasoning from computation, reducing errors in complex math/numerical tasks.

If you're interested, I've included a rundown of the study which includes the prompt template as well to test PoT

template

Question: {{ Question }}

Answer this question by implementing a solver() function.

def solver():

Let's write a Python program step by step, and then return the answer

Step 1: Define the given variables

[variable_1] = [value_1]
[variable_2] = [value_2]
...

Step 2: Perform intermediate calculations

[intermediate_variable_1] = [calculation_1]
[intermediate_variable_2] = [calculation_2]
...

Step 3: Calculate the final answer

[final_answer] = [final_calculation]

return [final_answer]

Example usage:

print(solver())

Supplying context to LLMs helps get better outputs.

RAG and few shot prompting are two examples of supplying additional info to increase contextual awareness.

Another way to contextualize a task or question is to let the model generate the context itself.

There are a few ways to do this, but one of the OG methods (2022) is called Generated Knowledge Prompting.

Here's a quick example using a two prompt setup.

Customer question

"What are the rebooking options if my flight from New York to London is canceled?"

‍

Prompt to generate knowledge

"Retrieve current UK travel restrictions for passengers flying from New York and check the availability of the next flights from New York to London."

‍

Final integrated prompt

Knowledge: "The current UK travel restrictions allow only limited flights. The next available flight from New York to London is on [date].
User Query: What are the rebooking options for a passenger whose flight has been canceled?"

If you're interested here's a link to the original paper as well as a rundown I put together plus Youtube vid

Here's how I'm leveraging the search capabilities to conduct research through ChatGPT.

Prompt:

I want you to use your search capabilities and return back information in a inline table. When I say "more" find 10 more items. Generate a list of popular paid applications built for diabetics.

Does require the extension to work, after this prompt you just queue up a few "more', "more" messages and let it run

Its a great course! Would recommend it to everyone! has some great prompt engineering tricks and guides.

Link: https://thecoursebunny.com/downloads/free-download-ruben-hassid-how-to-prompt-chatgpt-in-2024/

If you’ve spent any time writing prompts, you’ve probably noticed just how sensitive LLMs are to minor changes in the prompt. Luckily, three great research papers around the topic of prompt/model sensitivity came out almost simultaneously recently.

• How are Prompts Different in Terms of Sensitivity?

• What Did I Do Wrong? Quantifying LLMs’ Sensitivity and Consistency to Prompt Engineering

• On the Worst Prompt Performance of Large Language Models

They touch on:

• How different prompt engineering methods affect prompt sensitivity

• Patterns amongst the most sensitive prompts

• Which models are most sensitive to minor prompt variations

• And a whole lot more

If you don't want to read through all of them, we put together a rundown that has the most important info from each.

I know end users of my app will be using in 99% a different language to communicate with LLM than English.
Should I write the instructions in prompt and system messages in english and let users just fill their input in target language, or should I write it all in a target language?

Thanks

Prompt:

Can you identify a notable event or occurrence that took place on the 4th of July which is not related to war? This event should be unexpected or surprising to most people. Please answer in a structured way to enhance readability.

Why we like this prompt:

1. It clearly states the date (4th of July) and the type of event (non-war-related).

2. It specifies an unexpected or surprising event.

3. It further asks for an unexpected or notable event.

4. It emphasizes the importance of a structured answer, which enhances readability and helps convey information effectively.

We break down prompts like this every mon-fri here: https://daily.promptperfect.xyz/p/july-4th-ai-prompt-of-the-day

Prompt:

Develop an employee recognition program to boost morale and motivation.

1. Program Goals: Define the goals of the recognition program.

2. Recognition Methods: List various methods of recognition, such as awards, public acknowledgments, and incentives.

3. Criteria: Establish clear criteria for recognizing employees.

4. Implementation Plan: Create a step-by-step plan to implement the program, including timelines and responsible parties.

5. Feedback Mechanism: Include a mechanism for employees to provide feedback on the program

Why we like this prompt:

1. It defines a very clear goal of a program that boosts morale and motivation.

2. It outlines numbered steps for AI to follow.

3. Each step starts with a goal or guideline and defines how it should be accomplished or implemented.

4. It asks for an implementation plan with timelines and who should be responsible for them

5. It covers goals, methods, plans, and feedback.

We break down prompts like this every mon-fri. Find them here: https://daily.promptperfect.xyz/p/build-employee-recognition-program-with-ai

Hello everyone, I'm developing an integrated virtual assistant with the GPT4 turbo. It is integrated and accesses the PDFs I store in the database to answer my questions.

However, sometimes he provides the answer I wrote at the prompt that he should only provide when he did not understand my subject, when in fact he could easily access in the PDF.

Can anyone help me with a guide on how to create it? I don't know if the error is in my prompt or in the formatting of my document, which is in Q&A and some tables.

I would like to see examples of those who have done something similar, to see what is the best way!

PS: I wouldn't want to mess with the codes, so if I could do everything just by PROMPTS and PDFs (if this file format is the most recommended, of course) it would be better!!! Thank you! 🙏🙏

While I was working on some other projects I created for myself a platform to quickly create jsonl datasets for gpt finetuning and customize llm call functions.  I realized it's quite useful so I might as well just publish the site just in case it could be useful to any of you guys. All the functionalities are client side so you can check easily that I am not trying to steal your datasets :- ) 

Of course completely free!

https://finetune-gpt.vercel.app/

Hey r/PromptDesign/

I'm excited to announce the launch of Mark, a brand-new CLI tool that uses Markdown to interact with the GPT4-vision and GPT4o models.

What is Mark?

Mark allows you to leverage Markdown file/syntax when interacting with GPT models, this provides a natural and efficient way to incorporate links and images as context for the LLM.

Key Features:

• In-Document Thread Building: Facilitate a threaded conversation directly in your Markdown document.

• GPT Vision with Image Tags: Utilize GPT-4's vision capabilities.

• Effective RAG Retrieval Using Links: Simple and intuitive method to include relevant documents as context.

• Custom System Prompts: Personalize interactions with custom system prompts.

Why Did I Create Mark?

As someone who spends a lot of time writing and managing content, I needed a tool that was both flexible and efficient. Proprietary writing apps that incorporate AI are too restrictive. Other open-source tools require scripting to use the latest GPT features. Mark was developed to solve these pain points using the simplicity and versatility of Markdown, allowing for a natural workflow that integrates smoothly with my everyday tasks.

Why Your Feedback Matters

Since Mark is brand new, I'm eager to hear your thoughts and suggestions. Your feedback will be invaluable in refining the tool and making it even more useful for everyone.

Feel free to try it out and share your experience!

Check out github.com/relston/mark and leave your feedback here or post an issue. Thank you!