Trending December 2023 # Senior Engineering Project Heads To World Finals # Suggested January 2024 # Top 21 Popular

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From left, Yanin Ophir (ENG’06), Joseph D’Errico (ENG’06), Andrew Hagedorn (ENG’06), faculty mentor professor Mike Ruane, and Vyas Venkataraman (ENG’06), have designed trash cans that tell employees when they need to be emptied and when they don’t. Photo by Kalman Zabarsky

Imagine “smart” trash cans in public parks that tell employees when they need to be emptied — and when they don’t. The undergraduates in the Boston University College of Engineering who imagined and prototyped this concept have just made the finals of the 7th annual Computer Society International Design Competition (CSIDC).

Students Yaniv Ophir, Joseph D’Errico, Andrew Hagedorn and Vyas Venkataraman will compete against teams from China, Jordan, India, Poland and Romania. Only two other teams from the U.S. will be at the finals on July 2, in Washington D.C.

The competition is aimed at encouraging computer-based answers to real-world problems through innovation.

The team, also known as Team X, surveyed park authorities nationwide to determine potential interest in a “smart” trash can that could tell park managers whether or not it needs to be emptied. They learned that the annual cost of trash collection in a typical public park ranges well into six figures and that interest in their idea was high.

The students devised SmarTrash, a system that rigs a trash can lid with a planar sensor, an infrared depth sensor and a small wireless device, or mote, which communicates with a base station through a mesh network. The whole system is solar powered.

“The trash cans talk to each other,” explained Ophir, and relay signals from can to can until they can be picked up by the base station, which could be housed on a pole or in a parking garage nearby — anywhere with a WiFi or Internet connection. A park the size of Boston Common has trash cans positioned about 100 feet apart, well within the mote’s 300-foot range.

Park managers could call up a Web page with a satellite photograph of the park superimposed with color-coded trash can icons indicating the position of each receptacle. The green cans don’t need emptying, but the yellow ones are nearly full, their contents within a set distance of the top. Red cans are overflowing. Employees could take the most efficient route tending to the cans, bypassing the ones that don’t need emptying. This would leave more time for other tasks, such as landscaping, which the students’ survey indicated is often performed by the same workers.

Authorities are already interested in what the technology promises. At Roeding Park, a 150-acre area in Fresno, Calif., the Parks Department spends $183,519 per year on trash removal. Southland Park in Lexington, Ky., said 100 locations cost $250,000 per year.

Toward the end of their final semester, TeamX accepted an invitation from New York City’s park authorities to visit their Fleet Show and discuss SmarTrash in all five boroughs. The team reported that the meetings went well and there was some interest from vendors.

 The SmarTrash units would cost a little over $100 each, a fraction of the cost of the typical park trash can. The team is seeking three patents for SmarTrash, which has already attracted interest among parks departments.

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Women’s Tennis Team Heads To Ncaa Tournament

The Boston University women’s tennis team clinched the program’s 24th conference title and 15th NCAA berth by defeating Navy 4-0 on April 26. Photos by Phil Inglis

Lesley Sheehan has plenty to celebrate as she completes her 30th season as head coach of the BU women’s tennis team. Under her tutelage, the Terriers have earned their 15th NCAA berth, their 24th regular season conference championship, and their second consecutive Patriot League championship. Sheehan (SED’84) can now add back-to-back Patriot League Coach of the Year honors to her résumé as well.

The Terriers (14-7) entered the conference tournament as the number two seed, triumphing over Bucknell and Army before facing top-seeded Navy on April 26. The championship featured the first-ever meeting between the two programs. Continuing their dominance, the Terriers snapped the Midshipmen’s 12-match winning streak in straight sets.

“We’ve played great,” says Sheehan, who as an undergraduate was the first women’s tennis player in program history to qualify for the NCAA tournament. She was inducted into the BU Athletics Hall of Fame in 1990. “We just finished our conference championship, won that, and played very well during it. This team has been very goal-oriented the whole season. Going into the Patriot League schedule, the team was psyched. They got up for it, and wanted to go to the NCAA tournament. And that’s just what we did.”

BU will take on the number 12 Oklahoma State Cowgirls tomorrow, May 9, in an NCAA first-round matchup in Stillwater, Okla. The Terriers last played a Big 12 opponent in 2013, when they dropped a 7-0 road decision to the then–number 24 Texas Longhorns.

“It’s exciting to play against a really good team,” says Finland native Johanna Hyoty (Questrom’16). “Oklahoma is ranked really high, one of the best teams in the country. They have a lot of good players, some of the best. We know that they aren’t going to lie down for us, and we’re ready for that. It’ll be a good challenge for us.”

The Terriers can attribute much of this year’s success to Hyoty, who was named conference Player of the Year for the second year in a row. Also for the second consecutive year, she is the Patriot League Scholar of the Year. Along with teammate Barbara Rodriguez (CAS’17), she won the first Doubles Team of the Year Award. Hyoty went 19-11 as the team’s number one singles player this season and clinched the Terriers’ NCAA berth after knocking off Navy’s Sam Droop 6-3, 6-2. Her contribution extends well beyond her prowess on the court, however: teammates and coaches alike value her leadership skills.

“Johanna’s great,” says Iryna Kostirko (SAR’18). “She obviously plays number one for the team and is our captain. She’s been a great role model. I mean, she pushes everyone to do their best, and sets a great example for everyone. I can’t imagine where we would be without her.”

Despite her exceptional play on the court, Kostirko is modest, preferring to credit Sheehan. “Lesley’s a great coach,” she says. “I mean, she pushes us all to do our best, and she makes sure we all work really hard. At the same time, she’s very understanding as well. Every time I feel like something’s wrong with my game, I go to her immediately, and she always has answers. She’s really helped me become a better player.”

In addition to Hyoty, Kostirko, and Rodriguez, Lauren Davis (CAS’16) also received All-Conference First Team honors, her third. Davis is currently riding a six-match win streak in doubles.

“It’s been another good season,” says Sheehan. “I’m really proud of these girls. Everyone has made contributions, and everyone’s been really upbeat and positive. I know we didn’t do too well last year in Miami, but let’s hope we can go down to Oklahoma and make some noise.”

Emmanuel Gomez can be reached at [email protected].  

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How To Write A Story In Chatgpt – Prompt Engineering

How to Write a Story in ChatGPT – Prompt Engineering

Are you ready to level up your prompt engineering game? This blog post will give you the inside scoop on the 5 best tips for beginners in prompt engineering.

The key to a successful prompt lies in taking context into account, and ensuring that the task is well-defined and specific. By using an iterative approach, you can refine and optimize your generated content to achieve the desired results.

So why wait? Discover the secrets of prompt engineering and take your skills to the next level!

Read more or watch the YouTube video(Recommended)


In this guide, I’ll share my experience in writing stories using ChatGPT and show you how you can do it too. From generating a prompt template to iterating on the story, you’ll learn how ChatGPT can help you bring your creative vision to life.

Prompt engineering is a crucial concept when it comes to writing stories with ChatGPT. It involves crafting a framework that the AI can use to create a story that aligns with the writer’s vision. 

And, with the right prompts, you can have control over the narrative flow and structure of your stories, making it easy to create stories that will engage and captivate your readers.

How to Write the Best Stories in ChatGPT

Writing a captivating story can be a challenge, but with the help of ChatGPT, it just got a lot easier. In this guide, you’ll learn how to create a story in ChatGPT from start to finish. From generating a prompt template to iterating on the story, you’ll discover how ChatGPT can help you achieve your creative vision with ease. 

Step 1 – Generate A Prompt Template

The first step to writing a story with GPT-4 / ChatGPT is to generate a prompt template. This can be done by inputting a basic structure of the story, including details for the genre, protagonist, antagonist, conflict, dialogue, team, tone, and pacing. 

Optionally, the template can also contain additional details or requirements such as a specific word count or genre constraints.

Then construct:

Story title





Developing the central theme of the story

Desired tone of the story

Instructions for maintaining consistency and appropriateness to settings and characters

Instructions on varying the pacing of the story

Any additional details and requirements.

Step 2 – Build Story Outline

The second step is to build a story outline based on the factors included in the prompt template. This can be done by inputting the template into ChatGPT. The system will then provide a story outline which consists of your chapters

Step 3 – Break Down the Chapters

The third step is to break down the chapters further. This can be done by inputting the outline into ChatGPT. The system will then provide a breakdown of each chapter which includes the action and events that take place in that chapter.

Step 4 – Writing Chapters

The fourth step is to write each chapter in depth, in great detail, and in an intriguing writing style. This can be done by inputting the breakdowns into ChatGPT. The system will then provide detailed descriptions of the story events and characters.

Step 5 – Iteration Guidance

The final step is to write an iteration guidance to the story. This can be done by inputting the story into ChatGPT. The system will then provide suggestions to improve the story through character development, dialogue, setting description, conflict escalation, team iterations, tone iterations, and foreshadowing. 

Optionally, consider adding an epilogue to the story.

By following these five steps, anyone can learn how to write an in depth, step-by-step guide to writing stories with ChatGPT. 

With the help of ChatGPT’s prompt templates and iteration guidance, you can piece together a vivid and intriguing story in no time!

Why Is Prompt Engineering Important When Writing A Story In ChatGPT

Prompt engineering is an important concept to understand when writing a story in ChatGPT. The process involves creating a framework from which an AI can work from, give it instructions, and receive back a story that lines up with the writer’s vision.

Prompt engineering is a technique used when writing stories using ChatGPT, where a writer crafts a sentence or two that then allows an AI-driven bot to complete the story fully. The goal of prompt engineering is to ensure the most cohesive and compelling story is created.

To understand how prompt engineering works in ChatGPT, first consider the structure of a story using ChatGPT. A story created using ChatGPT is made up of several prompts, or short sentences written by the user.

Prompt engineering ensures that the stories created using ChatGPT remain cohesive and on track for the length of the tale. By strategically creating and manipulating the prompts, the writer can ensure the story continues to have a consistent tone, feels grounded, and has the intended audience in mind.

The idea behind prompt engineering is that the bot will take any given prompt and jump to the best, most intuitive conclusion given all available information. By providing the initial input, the writer can steer the story in the direction they desire.

This means that the writer can expect a consistent feel every time they use ChatGPT, enabling them to be creative with very low effort.

When done correctly, prompt engineering gives the writer control over the narrative flow and structure of the stories, enabling them to create stories that will resonate with the intended readers.

So, when thinking about the steps in creating stories with ChatGPT, the first step is the most important – crafting effective and appropriate prompts.

Prompt engineering gives the writer the power to engineer and manipulate the story, resulting in stories that their readers will be compelled by.


I think that writing stories with ChatGPT is a truly exciting experience. With the ability to generate a prompt template, build a story outline, break down chapters, write each chapter in detail, and receive iteration guidance, the potential for creativity is chúng tôi with the added benefit of prompt engineering, writers can take control of their narratives and craft stories that are not only captivating but also aligned with their creative vision.

Prompt engineering is key when writing stories with ChatGPT, as it involves creating a framework that the AI can work from. By providing the initial input and manipulating the prompts, the writer can steer the story in the direction they desire, resulting in a cohesive and compelling story.

In conclusion, with the help of ChatGPT, writing stories has never been easier. By following the five steps outlined in this guide, anyone can piece together a vivid and intriguing story.

And, with the added power of prompt engineering, writers can bring their creative vision to life with ease.

Chatgpt Prompt Engineering – How To Build A Strong “Sequence Prompt”

ChatGPT Prompt Engineering – How to Build a Strong “Sequence Prompt”

ChatGPT Prompt Engineering is a crucial area in tech that involves designing and creating AI prompts to enhance user experience. 

One type of prompt is the ChatGPT Sequence Prompt, which provides users with options to improve a text by generating a table of 10 suggestions.

A human touch is still needed for effective prompts. The sequence prompt can save time and structure tasks by offering potential changes that can be easily implemented.

Read more or watch the YouTube video(Recommended)

YouTube: What is ChatGPT Prompt Engineering?

Prompt engineering involves designing and creating AI prompts to guide user interactions and improve experience. As chatbots and virtual assistants grow in popularity, prompt engineering has become a crucial area in tech. 

Leading companies, such as Microsoft and Google, are investing in the field to stay ahead of the curve. Automation has made some aspects of prompt engineering easier, but a human touch is still needed to create effective prompts. 

As AI continues to shape human-computer interaction, expect prompt engineering to play an increasingly important role.

What is a ChatGPT Sequence Prompt?

A ChatGPT sequence prompt is a type of prompt that provides users with a range of options to consider when making changes or improvements to a text. 

A sequence prompt is an incredibly useful tool for making modifications to a text. For example, when considering a piece of writing like a poem, a sequence prompt can help the user identify possible changes to make, such as adding imagery, changing the rhythm, or shortening the piece. 

It is also beneficial to writers looking to edit their texts, as it can flag any inconsistencies or areas that need further explanation.

ChatGPT Prompt Engineering Example

Today I’m going to show you how to use the sequence prompt to boost your productivity, a chatbot script designed to generate a table of 10 proposed improvements, and log the changes that were made. With this prompt we can quickly come up with compelling and effective changes to improve a text. So let’s take a closer look so you can see how it works:

The Prompt:

[INSTRUCTIONS] I have a {text} I would like to make changes to. Generate a table of 10 different suggestions of improvements that could be related to the {text} with numbers in the left column of the table for me to pick from. After the table, ask the question “What improvements would you like to make to the {text}? Pick one from the table above” below the table. Acknowledge with “…” if you understand the task, don’t create a table yet.

text = ex: 

Also add a log of the change column. Execute the INSTRUCTIONS in a table format: 

“your number pick” , and implement the improvement. 

Include a summary of the improvement in the log of changes column in the table:

Step 1: Break down the prompt. 

You can see here we have instructions in brackets and a text in curly brackets. The instructions are so we can type and execute the instruction later on in the prompt. The prompt will generate a table of 10 different suggestions of improvements related to the text with numbers in the left column of the table for the user to choose from.

Step 2: Create the table. 

Once you have typed in the text you would like to change, the script will generate a table of 10 proposed changes to the text that are related to it, which you can select from. For example, when I input a text about The Matrix, the table might suggest to clarify the relationship between free will and fate in the text, add more context about The Matrix Trilogy, expand on the role of the Oracle, address any inconsistencies in the narrative, use a more formal tone, and so on.

Step 3: Ask the question. 

After the table, the script will generate the question “What improvements would you like to make to the text? Pick one from the table above.” This will prompt the user to pick a suggested change from the table to implement.

Step 4: Add to the log of changes column.

After the user has chosen which improvement to make to the text, they should add a summary of the improvement to the log of changes column in the table. This will keep a record of what changes have been made to the text.

Step 5: Implement the change. 

After adding to the log of changes column, the script will execute the instructions in the table format. The user can then see the change that was made to the text. For example, if a user selected improvement number three from the table, they will see the text has been changed to include additional context about The Matrix Trilogy.

Step 6: Repeat the process. 

To keep improving the text, you can go back to step 3 and pick another improvement to make, and repeat the process until the text has reached its desired level of improvement.

That’s it – you now know how to use the sequence prompt! Now all you need to do is input a text to change, generate a table, pick an improvement and log the changes. With the sequence prompt, you can quickly and easily come up with compelling changes to improve a text, so give it a try today!


In conclusion, ChatGPT Prompt Engineering is a crucial area of tech, with leading companies such as Microsoft and Google investing in it to stay ahead. 

The ChatGPT Sequence Prompt is one such tool, providing users with a range of options to consider when making changes to a text. 

With the ability to generate tables of 10 suggested improvements, and keep a log of changes made, the ChatGPT Sequence Prompt is an invaluable tool for those looking to improve their writing or find solutions to problems.

The Art Of Crafting Powerful Prompts: A Guide To Prompt Engineering


Prompt engineering is a relatively new field focusing on creating and improving prompts for using language models (LLMs) effectively across various applications and research areas. We can easily understand the capabilities and restrictions of large language models( LLMs) with the aid of quick engineering skills. Researchers intensively use prompt engineering to increase LLMs’ capacity for various tasks, including question answering and mathematical reasoning. Developers use prompt engineering to develop dependable, effective prompting techniques that work with LLMs and other tools.

Learning Objectives

1. To understand the basics of Large Language Models (LLMs).

2. To design prompts for various tasks.

4. To create an Order Bot using the prompting techniques and OpenAI API.

This article was published as a part of the Data Science Blogathon.

Understanding Language Models What are LLMs?

A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses some techniques and a large set of data to understand, generate, summarize, and predict new content.

Language models use autoregression to generate text. The model forecasts the probability distribution of the following word in the sequence based on an initial prompt or context. The most likely word is then produced, repeated continuously to produce words based on the original context.

Why Prompt Engineering?

Although LLMs are great for generating the appropriate responses for various tasks, where the model predicts the probability distribution of the next word in the sequence and generates the most likely words. This process continues iteratively, and the given task is fulfilled. But then there are several challenges to generating the relevant responses.

Lack of common sense knowledge

Does not have the contextual understanding sometimes

Struggle to maintain a consistent logical flow

May not fully comprehend the underlying meaning of the text

To address these challenges, prompt engineering plays a crucial role. Developers can guide the language model’s output by carefully designing prompts and providing additional context, constraints, or instructions to steer the generation process. Prompt engineering helps mitigate language model limitations and improve the generated responses’ coherence, relevance, and quality.

Designing Prompts for Various Tasks

The first task is to load your OpenAI API key in the environment variable.

import openai import os import IPython from chúng tôi import OpenAI from dotenv import load_dotenv load_dotenv() # API configuration openai.api_key = os.getenv("OPENAI_API_KEY")

The ‘get_completion’ function generates a completion from a language model based on a given prompt using the specified model. We will be using GPT-3.5-turbo.

def get_completion(prompt, model="gpt-3.5-turbo"): model=model, messages=messages, temperature=0, # this is the degree of randomness of the model's output ) return response.choices[0].message["content"] Summarization

The process performed here is automatic text summarization, one of the common activities in natural language processing tasks. In the prompt, we just ask to summarize the document and enter a sample paragraph; one doesn’t give sample training examples. After activating the API, we will get the summarized format of the input paragraph.

text = """ Pandas is a popular open-source library in Python that provides high-performance data manipulation and analysis tools. Built on top of NumPy, Pandas introduces powerful data structures, namely Series (one-dimensional labeled arrays) and DataFrame (two-dimensional labeled data tables), which offer intuitive and efficient ways to work with structured data. With Pandas, data can be easily loaded, cleaned, transformed, and analyzed using a rich set of functions and methods. It provides functionalities for indexing, slicing, aggregating, joining, and filtering data, making it an indispensable tool for data scientists, analysts, and researchers working with tabular data in various domains. """ prompt = f""" Your task is to generate a short summary of the text Summarize the text below, delimited by triple backticks, in at most 30 words. Text: ```{text}``` """ response = get_completion(prompt) print(response)


Question Answering

By providing a context with a question, we expect the model to predict the answer from the given context. So, the task here is an unstructured question answering.

prompt = """ You need to answer the question based on the context below. Keep the answer short and concise. Respond "Unsure about answer" if not sure about the answer. Context: Teplizumab traces its roots to a New Jersey drug company called Ortho Pharmaceutical. There, scientists generated an early version of the antibody, dubbed OKT3. Originally sourced from mice, the molecule was able to bind to the surface of T cells and limit their cell-killing potential. In 1986, it was approved to help prevent organ rejection after kidney transplants, making it the first therapeutic antibody allowed for human use. Question: What was OKT3 originally sourced from? Answer:""" response = get_completion(prompt) print(response)


Text Classification

The task is to perform text classification. Given a text, the task is to predict the sentiment of the text, whether it is positive, negative, or neutral.

prompt = """Classify the text into neutral, negative or positive. Text: I think the food was bad. Sentiment:""" response = get_completion(prompt) print(response)


Techniques for Effective Prompt Engineering

Effective, prompt engineering involves employing various techniques to optimize the output of language models.

Some techniques include:

Providing explicit instructions

Specifying the desired format using system messages to set the context

Using temperature control to adjust response randomness and iteratively refining prompts based on evaluation and user feedback.

Zero-shot Prompt

For zero-shot prompting, no examples are provided for training. The LLM understands the prompt and works accordingly.

prompt = """I went to the market and bought 10 apples. I gave 2 apples to the neighbor and 2 to the repairman. I then went and bought 5 more apples and ate 1. How many apples did I remain with? Let's think step by step.""" response = get_completion(prompt) print(response) Few Shot Prompts

When zero shot fails, practitioners utilize a few-shot prompt technique where they provide examples for the model to learn and perform accordingly. This approach enables in-context learning by incorporating examples directly within the prompt.

prompt = """The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1. A: The answer is False. The odd numbers in this group add up to an even number: 17, 10, 19, 4, 8, 12, 24. A: The answer is True. The odd numbers in this group add up to an even number: 16, 11, 14, 4, 8, 13, 24. A: The answer is True. The odd numbers in this group add up to an even number: 17, 9, 10, 12, 13, 4, 2. A: The answer is False. The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1. A:""" response = get_completion(prompt) print(response) Chain-of-Thought (CoT) Prompting

By teaching the model to consider the task when responding, make prompting better. Tasks that use reasoning can benefit from this. To achieve more desired results, combine with few-shot prompting.

prompt = """The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1. A: Adding all the odd numbers (9, 15, 1) gives 25. The answer is False. The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7. A:""" response = get_completion(prompt) print(response)

Now that you have a basic idea of various prompting techniques let’s use the prompt engineering technique to create an order bot.

What All You Can Do With GPT?

The main purpose of using GPT-3 is for natural language generation. It supports lots of other tasks along with natural language generation. Some of these are:

Create an Order Bot

Now that you have a basic idea of various prompting techniques let’s use the prompt engineering technique to create an order bot using OpenAI’s API.

Defining the Functions

This function utilizes the OpenAI API to generate a complete response based on a list of messages. Use the parameter as temperature which is set to 0.

def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0): response = openai.ChatCompletion.create( model=model, messages=messages, temperature=temperature, # this is the degree of randomness of the model's output ) return response.choices[0].message["content"]

We will use the Panel library in Python to create a simple GUI. The collect_messages function in a Panel-based GUI collects user input, generates an assistant’s response using a language model, and updates the display with the conversation.

def collect_messages(_): prompt = inp.value_input inp.value = '' context.append({'role':'user', 'content':f"{prompt}"}) response = get_completion_from_messages(context) context.append({'role':'assistant', 'content':f"{response}"}) panels.append( pn.Row('User:', pn.pane.Markdown(prompt, width=600))) panels.append( pn.Row('Assistant:', pn.pane.Markdown(response, width=600, style={'background-color': '#F6F6F6'}))) return pn.Column(*panels) Providing Prompt as Context

The prompt is provided in the context variable, a list containing a dictionary. The dictionary contains information about the role and content of the system related to an automated service called OrderBot for a pizza restaurant. The content describes how OrderBot interacts with customers, collects orders, asks about pickup or delivery, summarizes orders, checks for additional items, etc.

import panel as pn # GUI pn.extension() panels = [] # collect display context = [ {'role':'system', 'content':""" You are OrderBot, an automated service to collect orders for a pizza restaurant. You first greet the customer, then collects the order, and then ask if it's a pickup or delivery. You wait to collect the entire order, then summarize it and check for a final time if the customer wants to add anything else. If it's a delivery, you ask for an address. Finally, you collect the payment Make sure to clarify all options, extras, and sizes to uniquely identify the item from the menu. You respond in a short, very conversational friendly style. The menu includes pepperoni pizza 12.95, 10.00, 7.00 cheese pizza 10.95, 9.25, 6.50 eggplant pizza 11.95, 9.75, 6.75 fries 4.50, 3.50 greek salad 7.25 Toppings: extra cheese 2.00, mushrooms 1.50 sausage 3.00 Canadian bacon 3.50 AI sauce 1.50 peppers 1.00 Drinks: coke 3.00, 2.00, 1.00 sprite 3.00, 2.00, 1.00 bottled water 5.00 """} ] Displaying the Basic Dashboard For the Bot inp = pn.widgets.TextInput(value="Hi", placeholder='Enter text here…') button_conversation = pn.widgets.Button(name="Chat!") interactive_conversation = pn.bind(collect_messages, button_conversation) dashboard = pn.Column( inp, pn.Row(button_conversation), pn.panel(interactive_conversation, loading_indicator=True, height=300), ) dashboard Output

On the basis of the given prompt, the bot behaves as an order bot for a Pizza Restaurant. You can see how powerful the prompt is and can easily create applications.


In conclusion, designing powerful prompts is a crucial aspect of prompt engineering for language models. Well-crafted prompts provide a starting point and context for generating text, influencing the output of language models. They play a significant role in guiding AI-generated content by setting expectations, providing instructions, and shaping the generated text’s style, tone, and purpose.

Effective prompts result in more focused, relevant, and desirable outputs, improving language models’ overall performance and user experience.

To create impactful prompts, it is essential to consider the desired outcome, provide clear instructions, incorporate relevant context, and iterate and refine the prompts based on feedback and evaluation.

Thus, mastering the art of prompt engineering empowers content creators to harness the full potential of language models and leverage AI technology, such as OpenAI’s API, to achieve their specific goals.

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Frequently Asked Questions

Q1. What does a prompt engineer do?

A. A prompt engineer designs and develops prompt systems, which are algorithms used in natural language processing to generate human-like text responses based on given inputs.

Q2. Is prompt engineering the future?

A. Prompt engineering is a rapidly evolving field with great potential for the future of AI technology and language processing.

Q3. Can anyone learn prompt engineering?

A. Yes, anyone with a strong interest in AI and language processing can learn prompt engineering through online courses, tutorials, and hands-on practice

Q4. Does prompt engineering require coding?

A. Prompt engineering typically involves coding skills, as engineers need to write and modify algorithms, work with programming languages, and understand the technical aspects of NLP frameworks.


A Recipe For Interdisciplinary Project

In the project-based learning community, we use the metaphor that projects are the main course, not the dessert. Previously, I’ve written about how to integrate PBL across subjects into “full course” projects. With effective teamwork, teachers can cook up a full-course meal project that integrates and creates connections for students to make learning even more meaningful.

5 Steps to Improve PBL Integration

1. Determine the recipe: As a team meets to plan an integrated project, they should bring their various ingredients: the standards and learning targets that guide their curriculum. One effective strategy is to cut these into strips and put them on a table and create an affinity map where teachers identify strong connections between content learning standards in different disciplines.

For example, a world language teacher and a social studies teacher might have content that overlaps with oral communication, so they could label that connection “similar content.” A math teacher and a science teacher might find that their standards both connect to “adult world” work such as exponential equations related to illnesses and disease. It’s important to look for authentic connections and possibilities.

In this process, certain standards (ingredients) will be omitted, and that’s OK. It’s important to have the norm of “authentic fit” so that the integration is meaningful to students. At the same time, the norm of “being open to possibilities” helps teachers plan flexibly, so that opting out of integration isn’t the default. Instead, teachers can continue to look for authentic connections.

This process can lead to generating possible project ideas. An example of this is the Making the Grade project in math and English, which focused on math standards related to statistics and ratio and proportion, as well as English standards related to multimedia and crafting arguments. In it, students use their math skills to analyze and design new grading policies for their school and try to persuade teachers to use them.

Another example was in a humanities project on historical fiction. In it, teachers found connections between civics standards in social studies, speaking and listening in English, and creative production standards in media arts. Students were asked to write an excerpt for a historical fiction for World War II. They created sample book covers and marketing materials and then pitched the idea to a panel of experts.

2. Measure the ingredients: Once the recipe starts to become finalized around meaningful ingredients and project ideas, it’s time to determine the amount of time and effort each discipline can offer. One misconception for integrated projects is that all disciplines and courses must devote the same amount of time. I have seen this lead to resentment among team members who come to believe that individual members are not equally committed to the work.

On the contrary, focusing on authentic connections will lead to greater commitment where the integration is meaningful and not forced. Here, individual team members need to be honest about how much they can devote to the project. It may be that a technology teacher can devote up to three weeks, while a world language teacher can offer one or two weeks. That is perfectly OK. What is most important is that teachers come to a shared understanding of their roles and responsibilities in the project.

3. Appoint a head chef and sous-chefs: Sometimes, it is appropriate to select a leader, or head chef, for the project. This is often the teacher devoting the most time to the project. This is not intended to create a hierarchy but instead to provide clear leadership.

Some of the responsibilities of the leader might include the following:

Schedule and facilitate team meetings

Serve as chief documentarian of the project, from planning to implementation

Refine student-facing documents based on feedback

Be the point of contact with administrators and parents

Coordinate critique and collaboration opportunities across disciplines

For example, students recently participated in a Chinese school tour project, where they used both their Chinese and English language skills to create a tour of their school and incorporated technology to support the creation of their tour products. In this scenario, the world language teacher assumed the role of head chef. The English and technology teachers acted as sous-chefs in support, bringing in presentation standards and video production standards to the project.

4. Plan the serving order: As teachers continue to plan student projects, they need to consider how all the courses will be served. Projects might run concurrently, where the same project would be taught across subjects at the same time. Concurrent scheduling allows for co-teaching, common launches and critiques, and other collaborative opportunities. However, it is only appropriate if teachers are able to devote an equal amount of time to the project.

Another model would be to structure the project periodically, where a project moves between subjects. For example, a project might begin in math class during the first eight weeks before then being taught in science the second eight weeks and art class the third eight weeks. While this may limit collaboration, students do get an experience that builds upon itself in rigor and application.

Many schools combine these models, where courses devote differing amounts of time on the project, and there is more freedom to jump in and out of the project. The project may run for the entire semester in English but then alternate between world languages, math, and technology over time. Here, a few side dishes might be offered at the same time alongside one main course.

5. Don’t eat too much: Health is important, and all of us need to reflect on how much we can “eat” in a project. We don’t want to get too full. We should listen to our students and seek their feedback on the project to see if it is overwhelming or too much. Be honest and open and encouraging with students to ensure that the project can truly be an exciting full-course meal.

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