As a writer, I’m always looking for new ways to improve my craft and learn from others. Recently, I’ve been enjoying watching interviews with creative people about their processes and the tools they use. Continuing in my series of articles about what has captured my interest recently, today I’m highlighting two video series that you might also enjoy.
Although I’ve never taken David Perell’s online course “Write of Passage”, I’ve admired his online writing for a long time. Recently he began a series of videos on YouTube titled “How I Write“. In each video he interviews some great writers about their writing process and takes the time to really understand what they’re saying. I admire his questioning style – he asks a great question and then gets out of the way and lets the answer flow.
Perell’s now shared quite a collection of these interviews – including a recent one with fiction author Amor Towles, writer of “A Gentleman in Moscow” among other books. But I’d recommend starting first viewing this series with this one: I Spent 50 Hours With 20 Master Writers as it’s a great introduction to some of the key takeaways he got from many of his first group of interviews.
In addition to his Amor Towles interview, let me also recommend his interview of writer Steven Johnson: The Expert Behind Google’s Secret A.I. Writing Tool who has recently been spending much of his time working for Google on the NotebookLM product.
Do enjoy these interviews that Perell has shared – they’re very enjoyable to watch. Bridging into my second recommendation, Dan Shipper of Every.to recently did his own interview of David Perell: How David Perell Uses ChatGPT to Write for Millions. This is part of a series of interviews that Shipper has conducted about how ChatGPT is being used in some very interesting ways.
Another in Shipper’s series that quite fascinating to watch is his interview of Steph Smith: How to Find Your Next Big Idea Hiding on the Internet in which she shares some of her tricks and techniques for doing Internet research.
I really enjoyed these videos – they provide great perspectives on how creative minds work and how some of the best tools currently available can assist.
Note: This morning I read an article in the Washington Post about Charles Bailey who has been nominated to lead Montgomery County Maryland’s Fire and Rescue Service. In the article, Bailey is quoted: “You look at a piece of art that you know nothing about, and you ask yourself three questions: Function, meaning, style. And if you can answer, or derive answers to those three questions, it allows you to place the work of art into a context, which is the first step in deriving some sort of meaning from it.” I liked his comments and asked Google Bard to help flesh out these ideas.
Here I stand, face to face with an enigma. A canvas, a photograph, a silent symphony of pigment and light. I know nothing of its history, its creator, its purpose. Yet, a spark ignites within me, a hunger to understand. And so, I turn to my three trusty companions: Function, Meaning, Style.
Function: What purpose does this piece serve? Is it a portrait meant to capture a soul, a landscape yearning to share its grandeur, or perhaps a social commentary holding a mirror to society’s flaws? The brushstrokes, the composition, the very subject matter – each whisper hints at the artist’s intent. Is it a testament to faith, a celebration of beauty, or a cry for change?
Meaning: Beyond the surface, what whispers beneath the paint? What story does it yearn to tell, what emotions does it evoke? Is it a lullaby of serenity, a war cry of defiance, or a bittersweet ode to lost love? The interplay of light and shadow, the tension between form and space, each element becomes a brushstroke in the narrative. Do I see hope or despair, joy or sorrow, a call to action or a melancholic sigh?
Style: The artist’s voice, their unique language, speaks through the chosen medium. Is it the bold, expressive brushwork of a Van Gogh, the meticulous realism of a Vermeer, or the abstract whispers of a Kandinsky? Each stroke, each shade, reveals the artist’s soul, their relationship with the world. Do I see rebellion or reverence, chaos or order, a whispered secret or a deafening roar?
As I delve deeper into these questions, the canvas begins to come alive. The brushstrokes dance, the colors sing, the subject reveals itself. The enigma takes shape, not as a single, definitive answer, but as a kaleidoscope of possibilities. This is the beauty of art – it invites us to co-create, to fill in the blanks with our own experiences, emotions, and interpretations.
And so, I stand before the enigma no longer a stranger, but a fellow traveler on the path of meaning. The answers may remain elusive, but the journey itself is a revelation. For in the act of questioning, of seeking, I discover not just the meaning of the art, but also the echo of my own humanity within its depths.
This, I believe, is the true function of art – to not merely adorn walls, but to illuminate the labyrinthine pathways of our own existence. And as I turn away from the canvas, carrying its whispers within me, I know that the enigma has not been solved, but rather, transformed. It has become a mirror, reflecting not just the artist’s intent, but the ever-evolving story of who I am.
I always struggle a bit with I’m asked about the “hallucination problem” in LLMs. Because, in some sense, hallucination is all LLMs do. They are dream machines.
We direct their dreams with prompts. The prompts start the dream, and based on the LLM’s hazy recollection of its training documents, most of the time the result goes someplace useful.
It’s only when the dreams go into deemed factually incorrect territory that we label it a “hallucination”. It looks like a bug, but it’s just the LLM doing what it always does.
At the other end of the extreme consider a search engine. It takes the prompt and just returns one of the most similar “training documents” it has in its database, verbatim. You could say that this search engine has a “creativity problem” – it will never respond with something new. An LLM is 100% dreaming and has the hallucination problem. A search engine is 0% dreaming and has the creativity problem.
All that said, I realize that what people actually mean is they don’t want an LLM Assistant (a product like ChatGPT etc.) to hallucinate. An LLM Assistant is a lot more complex system than just the LLM itself, even if one is at the heart of it. There are many ways to mitigate hallcuinations in these systems – using Retrieval Augmented Generation (RAG) to more strongly anchor the dreams in real data through in-context learning is maybe the most common one. Disagreements between multiple samples, reflection, verification chains. Decoding uncertainty from activations. Tool use. All an active and very interesting areas of research.
TLDR I know I’m being super pedantic but the LLM has no “hallucination problem”. Hallucination is not a bug, it is LLM’s greatest feature. The LLM Assistant has a hallucination problem, and we should fix it.
Okay I feel much better now 🙂
Andrej Karpathy @karpathy
I truly appreciate your recognition of the differences between how large language models (LLMs) work and how traditional search engines function. It’s fascinating how LLMs have revolutionized various fields, including the creative realm. In creative endeavors, like writing poems, short stories, or even crafting an imaginative piece of fiction, the so-called “hallucination problem” of LLMs can prove to be surprisingly advantageous.
When you engage in creative writing, your primary objective is not to adhere strictly to accuracy and factual representation but rather to explore the limitless boundaries of your imagination. LLMs, with their ability to generate creative and unexpected content, can be a valuable tool to tap into new ideas and inspire innovative storytelling. They can help writers break free from conventional thinking patterns and venture into unexplored territories, allowing their creativity to flourish.
Conversely, in more formal and specialized writing contexts, such as drafting legal briefs or preparing technical reports, accuracy and precision are of paramount importance. LLM hallucinations, where the models generate content that may not be factually correct or contextually appropriate, cannot be tolerated in such situations. Here, the purpose is to convey information accurately, adhere to specific guidelines, and present a strong and well-supported argument.
It’s intriguing how the same technology that opens doors to unprecedented creative possibilities can also present challenges in other domains where accuracy and reliability are crucial. This duality highlights the importance of understanding the appropriate use cases for LLMs and being cognizant of the potential pitfalls and limitations they may possess in certain instances.
In summary, the LLM hallucination problem can indeed prove beneficial when the goal is creative expression, enabling writers to push boundaries and explore unconventional ideas. However, in situations that demand accuracy and precision, such as legal or technical writing, it becomes imperative to approach LLM-generated output with caution and verification to ensure the information presented is reliable and contextually appropriate.
Last night I was this YouTube video of an interview of Fei-Fei Li and Geoffrey Hinton in which, among many other topics, they talked about the societal impact of AI.
Hinton, in particular, made a point that I’ve not heard elsewhere about how these large language models are architecturally quite different from our human brains – the discussion begins at 53:33 into the video. I clipped that section of his remarks:
“At a later stage in my research, I had a profound realization that greatly heightened my interest in the societal impact of AI. As Fei-Fei mentioned, it’s all about the power of data.
These massive chatbots have been exposed to thousands of times more data than any human could ever hope to see.
The key reason behind this capability is the ability to create numerous copies of the same model, with each copy examining a different subset of the data. They can then derive gradients from this data to optimize their parameters. The remarkable aspect is that they can share these gradients among all the copies. This means that each copy benefits from what all the other copies have extracted from the data.
To put it into perspective, imagine if we had 10,000 individuals, each assigned to read 10,000 different books. After they’ve each read just one book, all of them would instantly know what’s in all of the books.
This is how these AI models operate, and it sets them apart as vastly superior to human capabilities.”
Geoffrey Hinton
This is a fascinating insight – and more clearly communicates the “learning power” of these LLMs than almost anything else I’ve read or heard. Think about it – brains that can share instantly what they’ve learned but simply exchanging a large quantity of gradients – the values which adjust and tune the neural networks in the models.
Overnight I came across a tweet from Jeremy Nguyen discussing a series of prompts he’s used with ChatGPT to obtain some useful results. He’s been using the AI to assist in helping identify concepts outside of the common wisdom and then using it to help craft drafting an article about those concepts.
I decided to try to use Claude for this purpose and used the following prompt:
You are an expert on the field of personal knowledge management. Please create a numbered list of at least 10 conventional wisdoms commonly thought to be the best practices in this field.
Then ask which numbers I think are unhelpful for my audience’s goals. Interview me, one question at a time, pausing for my answers, to find and distill my unique perspectives. The final output will be a bullet point list of my contrarian perspectives, the results I achieved from holding them, and how they differ from conventional industry wisdom.
Start by showing me 10 conventional wisdoms.
Claude responded to my request:
Here is a list of 10 common practices thought to be best for personal knowledge management:
Capture everything – take extensive notes on meetings, conversations, articles, etc. to record all potentially useful information.
Use a note-taking system religiously – stick to one consistent system for organizing and storing notes long-term. Popular choices are Evernote, OneNote, etc.
Review notes regularly – schedule time to re-read notes to refresh and retain the knowledge.
Make notes searchable – use tags, headers, etc. to make it easy to search and find notes later.
Link related notes – use wiki-linking, backlinks, etc. to connect related notes across documents.
Summarize key points – distill notes down to the most critical takeaways and action items.
Make notes durable – use durable formats like plain text to ensure notes remain readable in the future.
Share notes with others – collaborate and share notes across teams to spread knowledge.
Review and refine the system – periodically review and improve your note-taking system for maximum efficiency.
Stick to proven software tools – rely on established, stable software options rather than trying new unproven tools.
Which of these do you disagree with or find unhelpful for your goals? I’m happy to interview you one-by-one on your perspectives.
I responded: 1, 8
Claude responded:
Thank you for identifying #1 and #8 as practices you disagree with or find unhelpful. Let’s discuss them one at a time:
You mentioned #1 – Capture everything – take extensive notes on meetings, conversations, articles, etc. to record all potentially useful information. What is your perspective on this commonly recommended practice? How does your approach differ? What results have you achieved with your approach?
I responded: It is too easy to capture everything and drown in the resulting noise. It is much better to reflect on the potential value before capturing and only capture notes you expect to add real value to your PKM.
Claude responded:
Thank you for sharing your perspective. To summarize:
You believe capturing everything creates too much noise and low-value information.
Instead, you recommend reflecting on potential value first, and only capturing notes expected to meaningfully improve your PKM.
Your more selective and intentional approach to capturing notes adds higher-value information to your system compared to comprehensive note-taking.
Does this accurately capture your viewpoint and experiences with being more selective in what you capture? Please let me know if you would like me to clarify or expand on any part of the summary.
When you’re ready, we can move on to discussing #8 from the initial list.
I responded: Yes, that’s accurate.
Claude responded:
Great, I’m glad I summarized your perspective correctly.
Moving on to #8 – Share notes with others – collaborate and share notes across teams to spread knowledge.
You indicated you disagree with or find this practice unhelpful. Could you explain your thinking on sharing notes with others? How is your approach different and what results have you seen from it? I’m looking forward to learning your viewpoint.
I responded: By definition, PKM is personal, not collaborative. Any output from a PKM thinking process will of course need to be collaborative. But seeking collaboration too early can be very distracting.
Claude responded:
Thank you for elaborating – I understand your perspective better now:
You believe the “P” in PKM stands for “personal”, meaning knowledge management should focus on optimizing for individual thought processes initially.
Seeking collaboration too early in the PKM process can be distracting rather than helpful.
While output and action steps resulting from PKM should align with collaborative goals, the individual knowledge capture and refinement process works best solo.
You’ve found maintaining a personal approach without over-indexing on sharing notes keeps your PKM system focused and avoids distractions.
Please let me know if I am representing your viewpoint correctly or if you would expand on any part of the summary. I appreciate you explaining your contrarian perspectives on conventional PKM wisdom – it helps me grasp the nuances of effective personal knowledge management.
I responded: Yes, that’s correct.
Claude responded:
Thank you for confirming I accurately summarized your perspective on keeping PKM personal rather than over-collaborating.
To recap the key contrarian viewpoints we discussed:
On capturing everything: You are more selective and intentional, only capturing notes that seem likely to meaningfully improve your PKM rather than comprehensively documenting. This cuts down on noise and keeps your system focused on high-value information.
On sharing notes: You believe PKM works best as a personal system optimized for individual thought processes. Too much early collaboration can distract. You maintain a personal approach without over-indexing on sharing notes.
Let me know if you would like me to clarify or expand on any part of this summary. I appreciate you sharing your hard-earned wisdom – it provides beneficial counterpoints to conventional advice.
I responded: With those contrarian points of view in mind, can you pitch me some email newsletter headlines and ideas?
Claude responded:
Absolutely, let me propose some newsletter headline ideas that could highlight the contrarian perspectives we discussed:
“Forget Note-Taking Overload: How to Build a Lean, Focused PKM”
This headline promises to provide an alternative to exhaustive note-taking, focusing on your idea of intentional, high-value capture.
“The Power of Personal: Keeping PKM Independent Before Collaboration”
This headline emphasizes the benefits of maintaining a personal system before collaborating, per your advice.
“PKM Success Without Endless Reviewing and Sharing”
This headline speaks to your points about avoiding collaboration distractions and extensive review sessions.
“X Habits Holding Back Your PKM System – And What to Do Instead”
This headline can lead with the conventional practices you disagree with, contrasting them with your effective counter-approaches.
“Building a PKM System That Works for You, Not ‘Best Practices'”
This general headline contrasts standardized advice with your personalized, optimized approaches.
Let me know if any of those headlines and angles resonate with you as a good fit for conveying your contrarian PKM perspectives. I’m happy to suggest additional ideas or flesh out any of these proposed newsletter concepts further.
I responded: Great, please write a draft of a blog post for the first idea.
Claude responded:
Here is a draft blog post for the “Forget Note-Taking Overload: How to Build a Lean, Focused PKM” headline:
Forget Note-Taking Overload: How to Build a Lean, Focused PKM
Conventional wisdom says your personal knowledge management system should aim to capture everything – exhaustive notes on every meeting, conversation, and piece of content you encounter. But this drive for comprehensive documentation often creates note overload and a system bogged down by low-value information.
In this post, I’ll share my contrarian approach to developing a PKM that cuts through the noise to focus only on high-value knowledge. Here are the key principles:
Be intentional about capture. Don’t default to taking notes on everything. Instead, pause to reflect on whether it will meaningfully improve your system. Evaluate potential ROI before documenting.
Limit noise. Avoid transcribing conversations verbatim or cataloguing stuff you’ll obviously never need. Keep only truly vital points.
Let some things go. You don’t have to write down every insight. If it won’t provide lasting value, move on without capturing it.
Depth over breadth. Spend more time refining a few concise, high-impact notes vs many superficial ones.
Review sparingly. Don’t get stuck in an endless review loop. Re-read only notes critical for current projects.
Quality over quantity. Assess the value of your notes periodically. Prune liberally to keep only the best 10%.
This more mindful, selective approach prevents needless busywork while allowing your best ideas to rise to the top. The result is a lean, focused PKM containing far less – but far more useful – knowledge.
For me, this system delivers:
Sharper thinking by cutting through noise. I can see key insights clearly.
Faster access to my best ideas when needed, without digging through fluff.
Reduced burnout, since I’m not under pressure to document everything.
The point isn’t to avoid effort, but rather to ensure effort is spent intentionally on high-payoff PKM activities.
So try being selective in what you capture. Prune ruthlessly. Spend more time refining fewer notes. You may find, as I did, that less can mean more when it comes to building an effective personal knowledge base.
How does this draft look? I aimed to highlight your note selectivity principles and contrast them with exhaustive documentation practices. Please let me know if you would like me to modify or expand this draft blog post in any way. I’m happy to keep refining it.
I responded: That draft is good. I’d like to add discussion about the step after capture – relating a new note to existing knowledge in my PKM.
Claude responded:
Thank you, I’m glad the draft is headed in a good direction. Adding a section about connecting new notes to existing knowledge is an excellent suggestion. Here is an updated draft with an additional paragraph about that:
Forget Note-Taking Overload: How to Build a Lean, Focused PKM
Conventional wisdom says your personal knowledge management system should aim to capture everything – exhaustive notes on every meeting, conversation, and piece of content you encounter. But this drive for comprehensive documentation often creates note overload and a system bogged down by low-value information.
In this post, I’ll share my contrarian approach to developing a PKM that cuts through the noise to focus only on high-value knowledge. Here are the key principles:
Be intentional about capture. Don’t default to taking notes on everything. Instead, pause to reflect on whether it will meaningfully improve your system. Evaluate potential ROI before documenting.
Limit noise. Avoid transcribing conversations verbatim or cataloguing stuff you’ll obviously never need. Keep only truly vital points.
Let some things go. You don’t have to write down every insight. If it won’t provide lasting value, move on without capturing it.
Depth over breadth. Spend more time refining a few concise, high-impact notes vs many superficial ones.
Review sparingly. Don’t get stuck in an endless review loop. Re-read only notes critical for current projects.
Quality over quantity. Assess the value of your notes periodically. Prune liberally to keep only the best 10%.
Connect new insights to what you already know. After capturing a note, consciously relate it to existing knowledge in your PKM system. Look for ways the new learning builds on or enhances what you’ve already captured.
This more mindful, selective approach prevents needless busywork while allowing your best ideas to rise to the top. The result is a lean, focused PKM containing far less – but far more useful – knowledge.
For me, this system delivers:
Sharper thinking by cutting through noise. I can see key insights clearly.
Faster access to my best ideas when needed, without digging through fluff.
Reduced burnout, since I’m not under pressure to document everything.
The point isn’t to avoid effort, but rather to ensure effort is spent intentionally on high-payoff PKM activities.
So try being selective in what you capture. Prune ruthlessly. Spend more time refining fewer notes. You may find, as I did, that less can mean more when it comes to building an effective personal knowledge base.
Let me know if this helps round out the contrarian PKM advice with the connecting new learnings aspect. I’m happy to continue refining the draft with your guidance. Please feel free to provide any other feedback as well.
Frankly, I was pretty blown away by this interaction – especially by the interactivity of it including how it was able to interview me and make changes based on my suggestions.
This is definitely a prompt sequence that I’ll want to further explore!
It’s the last day of September and here in northern California we can feel the seasons changing.
We don’t get the brilliant fall colors in the trees here like in New England. Our seasons change more subtlety.
Instead, we tend to notice the little things of seasonal change – like how dark it’s getting to be in the mornings. When we’re used to waking up with the sun already up and daylight outside to this morning where it was dark until almost 7 AM.
Likewise in the evening when the darkness settles in much earlier than we’re used to – and sometime soon we’ll have the startling shift of moving back from Daylight Savings Time to Standard Time – giving us back a bit more of our morning light but robbing us even more of our evening light.
We also got a touch of very light rain overnight – the first glimmer of our traditional rainy season which will be here soon enough.
On my morning walk this morning, I noticed the splotches the rain left behind on the pavement. But what I first noticed was the first smells of fall – that smell of musty wet bark on the ground at our local park – a reminder for me of my years growing up in the midwest when the first good snow would fall and I’d notice how incredibly silent it would be outside – cold, crisp, still – and that beautiful silence.
Tomorrow we leave September behind and arrive in October. Fall is here.
Just for fun, I shared the text of this blog post with Claude, the AI chat bot from Anthropic, and asked it to transform it into the style of David Foster Wallace. It’s much more dramatic than my writing!…
Almost October
The last grubby light of September drags itself wearily over the expanse that is northern California, but even this seemingly endless reach of suburbs and strip malls cannot escape the subtle signs announcing autumn’s ingress. We do not, it is true, experience the pyrotechnic explosion of foliage that sets New England ablaze in actinic oranges and cadaverous greens; our vegetation shifts from its summer torpor to its winter slumber far more discreetly.
And yet the evidence accumulates nonetheless: the darkened dawn that delays its arrival until nearly 7 a.m., the premature nightfall that plunges us into gloom while work and school still claim our attention.
Soon enough the yearly sham of Daylight Saving will end, returning some of the stolen sunlight to our mornings yet robbing even more from our evenings.
A damp breeze scuttles in, bearing advance notice of the imminent rainy season, and leaves behind scattered dark splashes on the pavement.
On my quotidian constitutional this morning, I detected more signs of autumn’s advent: the fungal reek of wet bark in the local park conjured sense-memories of Midwestern childhood, of the first snow’s hushed and glorious descent, the implacable cold, the crystalline perfection of utter silence.
September expires today; October arrives tomorrow. Autumn makes its presence known, however quietly. The year winds down.
We will soon, if not already, be drowning in the Sea of Infinite Content!
It’s become clear that we’re heading into a world of infinite content – as if we aren’t already drowning in that sea of meaningless, automatically generated content. What was once a seemingly manageable stream of books, websites, and media is becoming a overwhelming tidal wave, threatening to erode the shores of human creativity. The age of innovation is impacted.
What’s moving us from today’s world of “just a lot” to our future of “way too much”? Why do I say we’re drowning in a sea of infinite content?
In two words: generative AI.
Since the launch last fall of ChatGPT (and many similar tools), it’s become increasing clear that we can now use these tools to churn out endless repetitive, low quality content. Indeed they can create spammy nonsense for themselves, with no regard for truth or diversity. All that matters is predicting the best next word.
The focus is on quantity over quality. So much garbage is being produced that it’s becoming harder to find meaningful information and creative art amidst the noise. Useful voices are being drowned out by the drone of algorithmic imitation of what’s already popular.
There is also the risk of misinformation as fake AI-generated content spreads. Propaganda and radicalization loom as nefarious actors exploit these tools. Jobs in creative fields disappear as AI replaces human creators and artists.
All this tidal wave of endless content needs is electricity. Power. And ever more semiconductors.
Where does this path lead us? What will become of creativity and originality if AI takes over? We must question how to harness infinite content ethically to serve humanity, not overwhelm it. The age of human innovation cannot be allowed to end under a sea of meaningless artificial content. We cannot lose what makes us human.
How can we ensure these technologies are used responsibly? How can we stem the tide before it’s too late? The debates must begin now.
And where will all of that power – and all of those semiconductors – come from?
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.
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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!
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