As part of his introduction of the new Ideaverse sample vault for Obsidian, Nick Milo has focused on the difference between projects versus efforts – and has settled on using the Efforts name for one of the folders in his new ACE folder structure. Efforts are different from projects and Nick believes that name is better suited to be part of a PKM system. In his fourth video introducing Ideaverse Nick Milo noted: “Why do some projects get done, and other projects get stuck? It might be because projects don’t work well with ideas.”
Of late I’ve enjoyed bouncing ideas around with Claude, the chatbot from Anthropic. I asked Claude to help me understand the differences between projects and efforts by writing a musing on that. I found Claude’s response quite useful:
Projects and efforts both require time, energy, and focus to accomplish something, but they have some key differences.
Projects are more defined, with a specific scope, timeline, and set of objectives. They have a clear beginning and a desired end result. Efforts are more open-ended, without rigid parameters. An effort may continue indefinitely as part of an ongoing process.
Projects follow a linear progression, with planning, execution, and completion phases. Efforts evolve organically, with starts and stops, detours, and gradual refinements. Projects aim to produce a deliverable, while efforts may have less tangible outcomes.
Projects can be handled individually or by teams. Efforts often involve loose collections of people contributing in diverse ways over time. Projects have project managers overseeing them. Efforts lack that formal hierarchical structure.
When projects finish, they are done. Efforts may pause or change direction, but they often persist in some form. We celebrate project launches and project completions. Efforts reflect continuous investment without fanfare.
Both projects and efforts require diligence and group coordination. But projects allow for more top-down control to keep work narrowly defined. Efforts rely more on bottom-up participation, following passion and drawing in a wide base of support.
In essence, projects impose structure on objectives, while efforts supply energy to ambitions. Both are vital and mutually reinforcing. A singular project can advance an expansive effort, just as an ongoing effort can spawn many delimited projects over time. By balancing closed-ended projects and open-ended efforts, we can direct our limited resources toward achievements with lasting impact.
Interacting with an AI assistant like Claude is a two-way conversation. To get the most out of your digital assistant, you need to know how to provide useful prompts that set it up for success. Follow these prompt writing tips when chatting with your AI:
Be Direct and Clear
Don’t beat around the bush when posing questions or requests to your AI assistant. Vague or ambiguous prompts will lead to vague and ambiguous responses from the AI. Get straight to the point and be as specific as possible about what you want it to do. Break down complex instructions into simple, step-by-step prompts to guide the AI.
Provide Sufficient Background
Don’t assume your AI assistant has any implied common sense or knowledge about your request. The AI only knows what’s in its training data! Give sufficient background and context in your prompts to set the AI up to respond appropriately. If you’re asking about a specific person or event the AI isn’t likely familiar with, give a brief explanation.
Ask Focused Questions
Open-ended questions can sometimes lead your chatbot down useless tangents. If you want specific info, ask specific follow-up questions rather than overly broad ones. You’ll get better results by being laser-focused on the information you need.
Check Responses and Follow Up
Your AI assistant is learning all the time. Check its responses to see if they actually provide what you were looking for, and follow up if it seems off track. You can rephrase your original prompt or ask clarifying questions to get the AI back on a useful path. Consider it a collaborative process.
Test the Limits
Feel free to get creative in testing the limits of your AI assistant’s capabilities. Novel prompts encourage the AI to expand what it can handle. Just be sure to provide plenty of guidance and background if you’re requesting something unusual or complex.
Give Your AI a Role to Play
When chatting with your AI assistant, it can be hugely beneficial to explicitly give it a role to play, such as teacher, travel agent, cooking assistant, or even friend. Defining a role provides necessary context that your AI needs to adapt its responses and language appropriately.
Without any framing, your assistant has no way of knowing how to act or what kind of information you need. A vague conversation with an undefined AI assistant can quickly go off the rails. But when you assign a clear role, it focuses the dialogue and reduces misunderstandings.
The role you give your AI doesn’t have to be rigid or limiting. Think of it as providing a general framing, tone, and personality that fits your needs for that particular conversation. Just tell your assistant “Act as my tutor and explain this concept to me.” Or “Pretend you’re a museum tour guide and describe this painting.”
Taking on a role allows your AI to access the right knowledge, terminology, and communication style to have a productive, natural dialogue. With the power of role-playing, your AI assistant can become an even more effective conversation partner!
Some Prompt Examples You Can Try
To see your AI assistant’s skills in action, give some longer, more descriptive prompts like these a try:
“Imagine you are an art museum tour guide named Sam leading a group through the impressionist gallery. In an enthusiastic but professional tone, explain the key characteristics of Monet’s painting style and describe what makes his Water Lilies painting a quintessential example of impressionism.”
“Act as a friendly chess tutor named Alex teaching me, a novice player, strategies for the first 10 moves as white in a classic game opening. Use simple, straightforward language to clearly explain the objectives of different standard openings and why they are solid foundational openings for white.”
“Pretend you are a home repair specialist named Jamie and I’m a customer needing help fixing my broken kitchen faucet. Walk me step-by-step through how to remove the faucet handle, disassemble and inspect the internal parts, replace any faulty washers or O-rings, and reassemble everything to get the faucet working again.”
“You’re an accomplished pastry chef named Pat teaching me how to bake chocolate chip cookies from scratch. Explain in a warm, patient tone the key steps from mixing dry and wet ingredients properly to getting the baking time and temperature right. Share any tips to ensure the cookies turn out perfectly chewy.”
“Imagine you are a professional genealogist named Gene helping me trace my ancestry. I’ve hit a roadblock finding records about my great-grandfather who I believe was born in Ireland around 1885 before immigrating to New York City. In a warm, encouraging tone provide step-by-step guidance for how to locate passenger manifests, naturalization records, birth/marriage/death certificates, and other documents that could shed light on his origins and immigration journey. Feel free to ask clarifying questions if you need any additional details from me. Your role is to patiently coach me through breaking through this genealogy brick wall using targeted record searches.”
Prompt for Haiku
To showcase your AI’s creative writing skills, try a prompt like:
“Write a haiku poem about viewing cherry blossoms in spring. Describe the delicate pink flowers blooming on branches and scattering in the wind. Convey a tranquil, reflective mood. Follow the 5-7-5 haiku structure with 3 lines and appropriate season word.”
This provides context about the haiku’s theme, asks the AI to use descriptive language and imagery, gives guidance on mood and structure, and defines the specific creative writing form. Framing poetic prompts in this way allows your AI to tap into its artistic expression. You can guide it to compose haiku, limericks, sonnets, and more on any topic you choose!
Giving longer prompts with more conversational detail and specific instructions allows your AI assistant to generate richer, more natural responses. Don’t be afraid to really set the scene and adopt a persona when framing your requests! For even more, see my post Multi-step prompting for Claude.
Please share what you’ve learned about effective prompts that you’ve discovered!
Credit: Drafted with the assistance of Claude, an AI assistant created by Anthropic.
On my walk this morning I listened to the latest episode of Cal Newport’s podcast Deep Questions which was about “creativity hacking”. He reviewed a couple of his techniques, shared a few links to good articles related to the topic, etc. For example, he talked about using different venues for working – such as moving between multiple locations in a single day. He cited several other techniques that he’s used all of which involved helping lift your mind out of the mundane to stimulate its ability to get creative.
Listening to Cal stimulated my mind to make the bridge between something like working on a whiteboard in front of a group of colleagues and, in lieu of humans, working on a topic with an AI chatbot like Claude, Bard, ChatGPT, etc.
I’m drawn to this approach because of just how low the overhead is to start to use chatbots for helping in my thinking and creativity processes. Unlike other humans, the chatbots are always available – 24×7 – and generally they’re pleasant and polite to interact with. Sometimes humans are too but they can also be unpleasant and impolite!
The speed of the interaction with a chatbot is probably slower than with another human or group of humans but I don’t find that to be an issue. In some ways, the ability to just pause, give me time to think, and then interact again with a chatbot is more pleasant and completely eliminates any peer pressure I might be feeling.
I can also use chatbots to respond to challenging questions I might ask or have it brainstorm wild ideas, or roleplay different perspectives – all of which help to get my creative juices flowing. A friend would do the same – but, again, the low overhead nature of creativity hacking with a chatbot is very attractive to me. I can also spread my chatbot interactions around between the various AI chatbots. They’ll bring different perspectives, just like a group of my friends might. I might also ask a chatbot to help me think about an idea from the perspective of a particular historical figure (e.g., “what would Steve Jobs say about this idea?”).
I’m increasingly finding the benefits of this chatbot approach helping me in my creative work. For example, while writing this blog post, I asked for a critique of it from one of the chatbots. Over a few interactions, I was able to gain some new insights to include which hopefully made it even better.
Update: this morning Andrew Chen posted a great article titled “How I use AI when blogging and writing” which explores his experiences using AI chatbots to help with blog posts, generating topic ideas/questions, and even starting a book outline. Definitely worth reading!
Having an interest in posting more to my blog, I’ve started playing with chatbots to help with ideas, etc. In particular, I’ve found Claude.ai to be quite a partner. Over the last few days, when I’ve come across an idea, perhaps a quote I liked, etc., I would flip over to Claude and start a prompt with: “write a musing on the following…”
Claude will quickly respond with a few paragraphs riffing on that idea. I can then add to it, perhaps argue with a point it made, bring in additional thoughts, etc. and it continues to help me flesh out my own thoughts that were triggered by that idea. Once I’ve had that conversation, I’ll copy it into Drafts and let it chill for a day or two before going back to it and seeing whether I want to pick it up for a blog post.
In my experience, Claude is better than ChatGPT, Google Bard, etc. for this kind of “conversation” with me. But I’m sure everyone’s mileage may vary – and it could also be that this kind of creative back and forth isn’t what you’re looking for or don’t find helpful. Again, for me, it all begins with challenging the chatbot to “write a musing on the following…”
I just started reading the book Unreasonable Hospitality by Will Guidara that was recommended on a recent podcast by Ted Seides. As a began reading the book, I was struck by his opening gambit:
Service Is Black and White; Hospitality Is Color.
In photography, I’ve learned to really appreciate the differences between black and white vs color images. When I started making images, I defaulted to color simply because that’s what digital cameras (and smartphones) do. Monochrome isn’t a natural mode for digital photography – it’s a post-processing technique that you might use for certain images. The difference can be striking between the color version vs the black and white version of the same image. I like to think of that conversion as one of reducing the “noise” in the image – color being often overwhelming to our senses. A conversion to monochrome changes the sense of the image to one more about light and shadow, composition, etc. Color on the other hand might be more useful in showing layering in an image.
So Guidara’s phrase “service is black and white; hospitality is color” really struck me. There’s more of a sense of good vs bad – or perhaps it’s good enough vs superlative in that phrase. I wanted to explore it a bit further outside of the book. As I’m doing more frequently these days when I want to do that, I asked a couple of my favorite chatbots for their thoughts on that statement. ChatGPT provided the following:
Certainly, the theme you’ve provided, “Service Is Black and White; Hospitality Is Color,” appears to draw a distinction between two related yet distinct concepts. It seems to suggest that while “service” might involve straightforward actions and procedures, “hospitality” brings a more nuanced and personalized dimension to interactions.
In the context of a restaurant, the theme “Service Is Black and White; Hospitality Is Color” takes on a compelling connotation. The differentiation becomes clearer when examining the roles that service and hospitality play in creating a memorable dining experience.
Service in a restaurant setting often involves the execution of predefined tasks and procedures, such as taking orders, delivering food, and processing payments. This aspect can be seen as “black and white” due to its standardized nature and the clear expectations associated with each step. A well-trained staff ensures efficiency and accuracy in these operational tasks, forming the foundation of a smoothly run establishment.
On the other hand, hospitality introduces the element of personalization and emotional connection to the dining experience, akin to the vibrant spectrum of colors. Hospitality encompasses the warmth, attentiveness, and genuine care that staff extend to patrons. It’s the art of making guests feel welcome, valued, and understood. This involves going beyond the basic service requirements to cater to individual preferences, anticipate needs, and create a memorable and delightful ambiance.
Hospitality adds that extra layer of authenticity that transforms a simple meal into an unforgettable event. The color it brings to the dining experience lies in the personalized interactions, the establishment’s unique ambiance, and the thoughtful gestures that make guests feel special. It’s about creating a narrative that goes beyond the mere transaction and leaves a lasting impression on the patrons.
In essence, while service forms the backbone of any restaurant operation, hospitality infuses it with personality, emotion, and connection. The interplay between the two is crucial; a well-balanced combination of efficient service and heartfelt hospitality is what elevates a restaurant from being merely functional to being an extraordinary destination that patrons want to return to.
This was a good discussion. I next turned to Claude:
Service gives customers what they paid for. Hospitality gives them an experience that exceeds expectations, one they’ll fondly remember and recommend to others. It builds an emotional connection through thoughtful gestures and anticipating needs.
While service is essential, hospitality is what transforms a transactional exchange into a meaningful relationship. Going beyond the black and white of service to offer the colorful hospitality touches conveys care and humanizes interactions, making people feel truly welcomed and valued.
I really liked this perspective and immediately began thinking about local restaurants in our area and where have we had dining experiences involving more than just good service – where we left feeling we really had a relationship with the place.
This was another step in my exploration of AI chatbots – how I’m beginning to see how to use them in ways well beyond simple searches on a search engine. I’m continuing to explore!…
I was intrigued by this sentence in a recent column by Bloomberg’s Matt Levine: The stereotypical way that a management consulting interview works is that you get asked questions like “how much pepperoni is sold in the US every year,” and then you reason your way to some estimate of the answer.
When I read this, I thought it would be fun to play with my favorite chatbots again and see if they might help me reason my way to an answer. I got very different results – not surprisingly. The prompt I used was:
The stereotypical way that a management consulting interview works is that you get asked questions like “how much pepperoni is sold in the US every year,” and then you reason your way to some estimate of the answer. Let’s reason step by step to try to find an answer to this question.
First, here’s what I got back from Google Bard:
Sure, here is a step-by-step process of how to reason our way to an estimate of how much pepperoni is sold in the US every year:
Gather some basic facts. We know that the average American eats 24 pounds of pizza per year, and that pepperoni is the most popular pizza topping, accounting for about 30% of all pizza orders. We also know that there are about 330 million people in the US.
Make some assumptions. We need to make some assumptions about how much pepperoni is used per pizza. A conservative estimate might be that each pizza uses 1 ounce of pepperoni. This means that the average American eats about 2.4 ounces of pepperoni per year.
Do some math. We can now estimate that the total amount of pepperoni sold in the US every year is about 800 million ounces. This is equivalent to about 250 million pounds.
This is just an estimate, of course, but it is based on some reasonable assumptions. It is likely that the actual amount of pepperoni sold in the US is higher than this, but it is also possible that it is lower. More accurate estimates could be made by collecting more data and making more refined assumptions.
Here are some additional factors that could be considered to improve the accuracy of the estimate:
The amount of pepperoni used per pizza can vary depending on the size of the pizza, the type of pizza, and the restaurant.
The popularity of pepperoni may vary from region to region in the US.
The amount of pepperoni imported into the US may also be a factor.
By considering these factors, it is possible to make a more accurate estimate of the amount of pepperoni sold in the US every year.
Next up was ChatGPT:
Of course, I’d be glad to guide you through the process of reasoning to estimate the amount of pepperoni sold in the US every year. It’s important to note that management consulting interviews often focus on assessing your problem-solving skills and ability to think logically. Let’s break down the problem step by step:
Step 1: Clarify the Scope Before we begin, it’s essential to clarify the scope of the question. Are we looking for the total weight of pepperoni sold, or the total revenue generated from its sales? This distinction will influence the data sources we’ll need to consider.
Step 2: Identify Relevant Data Sources To estimate the amount of pepperoni sold in the US, we’ll need to gather data from reliable sources. These could include industry reports, government statistics, market research firms, and food industry associations. Some possible sources might be the U.S. Department of Agriculture (USDA) and organizations like the American Meat Institute.
Step 3: Estimate Total Consumption One approach could be to start with the total meat consumption in the US and then estimate what percentage of that is likely to be pepperoni. You might need to consider factors such as the popularity of pizza, sandwiches, and other dishes that use pepperoni. Additionally, think about variations in consumption due to cultural and regional preferences.
Step 4: Break Down Consumption Break down the total estimated consumption of pepperoni into categories such as retail sales (supermarkets and grocery stores) and food service (restaurants, pizzerias, etc.). This can help refine your estimate by considering different distribution channels.
Step 5: Estimate Quantity per Serving and Servings Estimate the average quantity of pepperoni used per serving. For example, how many slices of pepperoni are typically used on a pizza or in a sandwich? Then, multiply this by the estimated number of servings sold in a year.
Step 6: Adjust for Factors Consider any factors that might affect the estimate, such as seasonal variations in consumption (for instance, more pepperoni might be consumed during holidays or certain events) or any recent trends that might have impacted consumption patterns.
Step 7: Calculate the Estimate With all these factors in mind, you can proceed to calculate your estimate. Make sure to show your calculations and assumptions clearly.
Step 8: Validate and Compare After arriving at your estimate, it’s a good practice to validate it against any available data, if possible. Compare your estimate to industry reports or official statistics to assess its accuracy.
Remember, the goal of this exercise is not necessarily to arrive at an exact figure, but to showcase your ability to logically reason through a complex problem using available information. Feel free to ask questions if you need further clarification on any of these steps.
Next up is Claude:
Okay, let’s break this down:
First, we need to estimate how much pizza is sold in the US each year. Pizza is a very popular food, so let’s assume that on average, each American eats pizza once per week. With a population of around 330 million, that’s about 330 million pizzas sold per week, or around 17 billion pizzas per year.
Next, we need to estimate what percentage of those pizzas have pepperoni. Pepperoni is a very common pizza topping, so let’s assume it’s on 50% of all pizzas sold. That brings us to around 8.5 billion pepperoni pizzas per year.
Now we need to estimate how much pepperoni is on each pizza. Let’s assume an average pepperoni pizza has around 50 pepperoni slices. If each slice is roughly 1 ounce, that’s about 50 ounces or around 3 pounds of pepperoni per pizza.
With 8.5 billion pepperoni pizzas per year, each with around 3 pounds of pepperoni, that gives us an estimate of around 25.5 billion pounds of pepperoni sold annually in the US.
Of course this is just a rough estimate with a lot of assumptions. The actual amount may vary quite a bit. But walking through the logical steps and making reasonable assumptions is generally the approach for these types of brainteaser questions. Let me know if this helps explain the thought process!
Finally is Bing Chat:
That’s an interesting question. To estimate how much pepperoni is sold in the US every year, we can use some assumptions and data from the web search results. Here are some possible steps to follow:
First, we can assume that most of the pepperoni sold in the US is used as a pizza topping, since it is one of the most popular pizza toppings.
Second, we can use the data from 7 to estimate how much pepperoni is used on a pizza of different sizes. For example, a 12-inch pizza uses about 1.75 ounces of pepperoni, and a 18-inch pizza uses about 3 ounces of pepperoni.
Third, we can use the data from 6 to estimate how many pizzas are sold in the US each year. According to this source, there are 3 billion pizzas sold in the US each year.
Fourth, we can use the data from 13 to estimate what percentage of pizzas ordered are pepperoni. According to this source, 36% of people order pepperoni on their pizzas.
Fifth, we can multiply the number of pizzas sold by the percentage of pepperoni pizzas, and then multiply by the average amount of pepperoni per pizza. To simplify the calculation, we can assume that the average pizza size is 14 inches, which uses about 2.5 ounces of pepperoni according to 7.
Therefore, the estimated amount of pepperoni sold in the US each year is:
3 billion x 0.36 x 2.5 = 2.7 billion ounces
To convert this to pounds, we can divide by 16, since there are 16 ounces in a pound. This gives us:
2.7 billion / 16 = 168.75 million pounds
This is our final answer, based on the assumptions and data we used. Of course, this is only an approximation, and there may be other factors that affect the actual amount of pepperoni sold in the US each year.
I hope this helps you understand how to approach this kind of question. Do you have any other questions for me? 😊
Bard estimated 250 million pounds. Claude estimated 25.5 million pounds. Bing estimated 168.75 million pounds. ChatGPT didn’t provide an estimate – it just discussed a methodology to get to an answer (which is what’s more important in a job interview!).
What’s the correct answer? Some quick Google searching finds results around 250 million pounds a year – closest to Bard’s answer.
What can we learn from this little exercise? I would have expected both Bard and Bing Chat to have an advantage as they’re both capable of doing web searches while ChatGPT (3.5) and Claude aren’t. Bing actually cites specific web references. But the most useful result actually isn’t the final value – all of the chatbots demonstrated steps to go through to get to an answer and it’s the ability to demonstrate those steps that answers the interview question. I’m sure by continuing each of the conversations I could have gotten more insights from each of the chatbots. Meanwhile, I’m now hungry for a pepperoni pizza!
There’s little new under the sun. Everything is a remix. As writers, we like to think our work is wholly original. But let’s be honest. All creative writing builds upon what came before it. Even the most innovative stories contain elements and ideas that the author over a lifetime was inspired by or outright borrowed from others.
The key is to steal wisely as a writer – to take existing concepts and put your own unique spin on them. Follow this three-step framework for effective remixing in your writing:
Study source material you enjoy to closely to identify specific aspects to borrow. Make sure you understand the context.
Extract the elements that appeal to you or support your goals. Don’t just copy verbatim. Be intentional.
Transform the borrowed elements by modifying, combining and recontextualizing. Add your own flair, your extensions, your new ideas.
All writers absorb influence from the world around them. You can take inspiration from the tone of a novel, the pacing of a movie, or the imagery used in a poem. Combine and rework these elements into something new.
For example, you could:
Use a Shakespearean soliloquy as inspiration for a monologue, modifying the language to be more modern.
Borrow storytelling techniques from folklore when crafting a fantasy saga, putting your own twist on the archetypes.
Adapt the fragmented style of modernist poetry when writing song lyrics.
The most innovative writers stand on the shoulders of those who came before them. As Austin Kleon says, “Nothing is completely original. Steal from anywhere that resonates with inspiration or fuels your imagination.” So don’t be afraid to openly steal – just be sure to transform and recontextualize rather than completely copy.
Study writing you admire and incorporate aspects into your own voice and vision. There are endless possibilities if you steal thoughtfully and fearlessly to fuel your creativity. By skillfully pilfering from the world around you, you can develop stories that feel uniquely your own. We do it all the time.
Some may argue this remixing approach borders on plagiarism. But there is an important distinction. Plagiarism is passing off someone else’s work in its entirety as your own. Strategic stealing involves taking select elements of a work and transforming them into something new through your personal creative lens. It’s about synthesizing a wide range of influences, not duplicating a single source. You put your own identifiable stamp on the end result.
Of course, you still need to credit your sources where appropriate. But influence and inspiration are not the same as plagiarism. Borrowing sparks of creativity from the world around you to generate innovative new writing is fair game and legal. As Pablo Picasso famously stated, “Good artists copy, great artists steal.” Masterful stealing will make you a better, more original writer.
Chatbots like Bard, ChatGPT and Claude can actually help facilitate this remix process and stimulate creative thinking to improve your writing. Here are five tips for putting chatbots to work as your creative muse:
Use them to brainstorm fresh ideas by describing a vague prompt and letting the chatbot riff on possibilities.
Ask them to rework a piece of writing in a radically different style or genre. The new perspective may spark inspiration.
Request examples of compelling imagery, metaphors, dialogue, etc. that you can borrow and rework.
Have them recombine elements from multiple sources into an original outline or draft you can develop further.
Ask for critiques of your writing to identify weak spots and areas for improvement you may not have noticed.
Chatbots won’t write the full piece for you, but they can assist at the ideation stage and provide seeds of inspiration through unique remixing. Don’t be afraid to steal any intriguing suggestions they propose! With practice, you’ll develop skills to remix writing on your own in innovative ways.
In fact, this whole blog post was co-authored by me and Claude based on some insights I took away from this article: Stealing Your Way to Original Designs! Matt Ragland‘s newsletter this morning pointed me to that article and made the initial connection. While reading it I was reminded of Austin Kleon’s earlier work “Steal Like an Artist”. Then I engaged Claude to help me draft this post. All in all, it took me about 10 minutes to write.
I’ve been experimenting with all of that available chatbots including Google’s Bard, OpenAI’s ChatGPT, Microsoft Bing Chat, and Anthropic’s Claude. A few days ago I was playing with Claude when I decided I’d try to learn more about multi-step prompting.
A recent video with Ethan Mollick emphasized that we tend to overemphasize initial prompts – looking for the “perfect prompt” – when what really adds value to a chatbot result isn’t the perfect initial prompt but rather the interaction you have with the chatbot. In other words, just like with an real intern, the conversational back and forth is what really adds to the learning and understanding from a session. I’ve begun applying this approach more regularly as I continue exploring these amazing new tools.
Below is the result of my back and forth conversation with Claude as I tried to learn more about the best practices for multistep prompting.
Introduction: The Power of Multistep Prompting
When conversing with Claude, you can get much more natural, detailed responses by using multistep prompts. This technique involves breaking down complex requests into multiple simpler follow-up prompts, rather than asking lengthy, dense questions all at once.
For example, instead of saying:
“Claude, can you tell me about the major battles of World War 2 fought by American troops in the European theater, with a focus on key events like the Normandy invasion?”
You would ask:
“Claude, what were the major battles American troops fought in during World War 2 in Europe?”
Then based on Claude’s initial response mentioning D-Day and the Battle of the Bulge you could ask focused follow-ups like:
“Can you provide more details on the D-Day invasion?”
“What happened at the Battle of the Bulge and why was it significant?”
This prompts Claude to provide more detailed, robust responses on a specific aspect of your original broad question. Chaining these bite-sized prompts together allows for a natural dialogue where you guide the conversation based on Claude’s replies.
The rest of this post will provide tips to help you get the most out of using multistep prompts to have engaging, productive conversations with Claude. Let’s get started!
Tip 1: Start with a Focused Initial Prompt
When first engaging Claude, begin with a clear, concise prompt setting the context. For example:
“Claude, can you recommend a good sci-fi movie from the last few years that has cool visual effects?”
Starting the conversation with a specific question about the type of movie you want gets better results than a vague prompt like “Let’s talk about movies.” It provides Claude with clear criteria to focus its initial response on recent sci-fi films with great special effects.
Tip 2: Ask One Question at a Time
Deconstruct big requests into individual follow-up prompts. Asking “What were the major events of WW2 and how did technology impact them?” overloads Claude. Instead try:
“What were the major events of WW2?”
“How did technology like radar and rockets impact these events?”
Simpler prompts allow more detailed, thoughtful responses from Claude.
Tip 3: Build on Previous Responses
Review Claude’s last response before asking your next prompt. For example:
You: “Who were Apple’s founders?”
Claude: “Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne.”
You: “What specific contributions did Steve Wozniak make to Apple?”
This builds a logical flow and conversation.
Tip 4: Clarify When Confused
If Claude’s response doesn’t make sense, politely ask for clarification before moving on:
You: “What was the impact of Midway on WW2?”
Claude: “The Battle of Midway enabled the Allies to gain the upper hand in the Pacific theater.”
You: “I don’t understand how Midway gave the Allies an advantage. Can you explain that more clearly?”
Don’t just accept unclear responses. Ask follow ups to get back on track.
Tip 5: Change Direction Based on Responses
Let Claude’s replies guide where you take the conversation next…
Tip 6: Periodically Summarize Key Facts
Restating important details helps ensure Claude stays on topic. For example:
You: “Can you tell me about the first airplane flight?”
Claude provides response.
You: “So the Wright brothers flew for the first time in 1903 in Kitty Hawk. What happened next in early aviation?”
Summarizing facts focuses the dialogue and confirms Claude’s understanding.
Tip 7: Keep Conversations 3-5 Prompts Long
Try to complete conversations within 3-5 focused prompts. For example:
Prompt 1: Ask for book recommendations
Prompt 2: Clarify if you want fiction or non-fiction books
Prompt 3: Ask for more details on a specific recommended book
Prompt 4: Thank Claude and end the conversation
While more than 5 exchanges often indicates a disjointed or unclear goal, I often end with a final prompt to the effect of “Is there anything else I should know or try to understand?” In other words, remember perhaps there’s “one more thing!”
Tip 8: Wrap Up with a Summary
End conversations by asking Claude to summarize its overall response:
You: “Can you please summarize the key points you mentioned about World War 2?”
This provides a clean conclusion to the dialogue.
Tip 9: Provide Feedback if Responses Seem Off
If Claude seems confused or makes incorrect statements, politely clarify to improve its training:
You: “I asked about D-Day, but your response seems to be about a different battle. Can you please double check your information about D-Day?”
This constructive input helps Claude improve.
Tip 10: Practice Makes Perfect
Have fun practicing multistep conversations on topics like movies, recipes, sports, current events, trivia, and product recommendations. The more you use prompts and follow-ups, the more natural conversations will become.
Taking It to the Next Level: Advanced Multistep Prompting
While effective for everyday conversations, multistep prompts can also enable advanced, niche uses of Claude you may not have considered:
Foreign Language Translation – Claude has some ability to translate between languages when you provide context. Try prompts like:
“Claude, can you translate this Spanish phrase into English: ‘¿Cómo estás?'”
Poetry – Claude can generate original poetry if you guide it. Prompt it to start a poem, suggest a topic, and refine the wording.
Research Assistance – Ask Claude to find and summarize information on obscure topics:
“Claude, can you research the ancient Sumerian civilization and summarize their key achievements?”
Creative Writing – Claude can craft short stories if you provide plot points and characters. Slowly build up a narrative through prompts.
Joke Telling – Give Claude a topic and have it come up with witty, original jokes through multistep exchanges.
Troubleshooting – Walk through a technical problem by describing symptoms and responding to Claude’s follow-up questions.
The possibilities are endless when you get creative with prompts! The key is providing context and steering the conversation to get Claude’s best work.
Conclusion: The Key is Conversation
The core takeaway is that multistep prompting allows for genuine back-and-forth conversation with Claude. Start applying these tips to your prompts and soon you’ll be chatting with Claude like an old friend! I’d love to hear of any other multistep best practices you discover. Now go have some fun conversations with Claude or your other favorite chatbot!