ChatGPT prompts for your PM work

these examples are sourced from lenny's newsletter https //www lennysnewsletter com/p/how to use chatgpt in your pm work https //www lennysnewsletter com/p/how to use chatgpt in your pm work go here for my best prompt hack prompt engineering plays a crucial role in enhancing the performance of ai language models and ensuring more accurate, relevant, and reliable outputs duplicate this document and continue adding your own! check how generatives models work collect and summarize user feedback and usage data reforge’s prd (product requirements document) toolkit docid\ hkcbhxt wcomaiypn5zvc synthesize survey results i'm going to send you a list of x survey responses to the question " " can you group these into buckets of insights, with their associated weight (in %), and list some examples of associated responses for each insight? \=== and the list of responses in the following message source https //twitter com/lucdid/status/1641538521113456644 find feature ideas and bugs from app store reviews i want to scrape app store reviews of our app using javascript walk me through it step by step then, i pasted all the reviews into the chat and asked gpt 4 to find the top 3 most requested features and complaints source https //twitter com/themaxlinus/status/1640396905069920272 how to get the review from app store or google play https //www uselumin co/review rocket extract insights from raw usage metrics converting our product usage metrics data to text and then just asking gpt questions about the data source https //twitter com/dotnetster/status/1640418988458131456 analysing lead funnel to identify conversion issues you can paste in the data in a table and ask for the conversion percentages and then ask scenario questions "what is the volume of raw leads that would be needed to generate x conversions?" source https //twitter com/tlonuqbar/status/1641712319423037440 come up with product name suggestions source https //twitter com/bailey jennings/status/1640392121533308928 source https //twitter com/jillianfunes/status/1641918295170334722 strengthen your argument come up with critical questions your audience may ask prd review "assume you are the cto, review this prd and give me critical, but fair feedback) before meetings "i'm meeting the vp of data science for a 30 minute call to discuss x what should i ask her?" after meetings "summarize my notes into minutes, action items" source https //twitter com/vybhavram/status/1640386133208223745 identify gaps and hidden assumptions in your thinking “what am i missing here?” “what am i being overly optimistic about?” “what is a macro event that can totally reverse the outcomes of this test” source https //twitter com/rishi kar/status/1640394447891333120 i am building collaboration software for users to create their mind maps and share them with their teams the following are the expected steps by a user 1\ user sees a sample mind map on our website 2\ user clicks on a button titled 'use this template' 3\ user signs up for an account and then makes their own mind map based on the template 4\ the user shares the mind map with their team members for review 5\ after completing the trial period, the user upgrades to a paid version considering the above steps as an ideal behaviour can you list out some assumptions that we are making about the user, their context and their motivations which might prevent them from upgrading to a paid plan? \=== that's a good start can you dig deeper and come up with 20 more assumptions think from a perspective of feasibility of building the software, usability of the software, desirability and viability at each step \=== thank you out of these, what are the top 3 assumptions that we should be focusing on also suggest some experiments or action items to mitigate their risk source https //twitter com/kranthitech/status/1640637300983087104 highlight edge cases and counterarguments 1 ‘tell me 5 reasons this feature won’t work as intended’ 2 ‘tell me 5 unintended consequences of this feature’ source https //twitter com/olivercitrin/status/1640443139403177984 steelmanning the other side of an argument when drafting a proposal (“why shouldn’t we do this?”) source https //twitter com/dagaadit/status/1640401753353994244 inspire roadmap ideas assist with roadmap ideation brainstorming use cases example "i'm considering implementing discounts in my ecom business we use shopify connected to sap as our erp what are the use cases i should document and review before writing up requirements?" source https //twitter com/nikita the ber/status/1640563578217398273 develop frameworks “come up with a framework for…” staring at a blank doc is sometimes the worst part source https //twitter com/brianrobeniol/status/1641100266698317826 come up with customer interview questions you are a {industry} product manager interviewing customers about {topic} objectives {objectives} please give me {number} questions to ask customers consider {supplemental information about industry/customers} source https //twitter com/kadenr1/status/1640417942532927488 inspire prds and user stories create a v1 prd or jira ticket prd writing write a product requirement document for an a/b test project for ("short a/b test project description") write jira tickets write a jira ticket with user story and acceptance criteria for ("short project description") source https //twitter com/patrickncho/status/1640433020573462528 identify drag metrics besides the basics like grammar i've found it to be very helpful in identifying counter metrics (or guardrail metrics) for the prd's success criteria prompt includes an overview of the feature, customer pain points, user stories, and success criteria source https //twitter com/tylerswartz/status/1640388016689782786 write user stories write user stories for onboarding process of a shipping app \=== create a detailed prd for a fixed audit asset management product called xyz \=== i need a cost benefit analysis on what it'll take to build \[product name] over a competittion product source https //twitter com/lydianwobodo/status/1640390294574100481 improve your writing p1 what would be a better way to say this add your version p2 what data points should i add to make my argument p3 in the above email, add another point related to this p4 say the same in a different way p5 polish this text "insert text" fun fact , you can also generate specific tones source https //twitter com/watstweetin2nyt/status/1640416242157625344 / source https //twitter com/vibhor chhabra/status/1641232257167036417 do market research how much did tv publisher x make from ad revenue vs cable subscription fees over the last five years source https //twitter com/phenly/status/1640407435360780297 ask technical questions sql queries source https //twitter com/irinai/status/1640765513499578370 programming specifics i'm a little rusty in r, how do i import this set of csv files and make a plot of this data over time? source https //twitter com/farrarscott/status/1640412596217212928 explain a broad concept explain \<topic> and cite your sources source https //twitter com/carnage4life/status/1640360288930390016 write the code for you write a react function which is a button that when you click it, it downloads svg source https //twitter com/rubychilds/status/1640513899010183169 create a v1 landing page i am designing the landing page for a collaborative knowledge workspace software called \[insert product name] it helps individuals and teams \[insert benefits] can you help me draft the copy for the landing page? separate sections such as \[headings], \[features], \[ctas] this is our current website \[insert full website copy as text] make it more personal and less generic the target group are developers, so don't make it too salesy focus on value prop, keep the language simple source https //twitter com/fstanev/status/1640395752991080482 create a v1 pitch deck can you provide an example of a bottom up market analysis for a tech product source https //twitter com/lessardcretech/status/1640408201538707456 and, maybe most importantly, help you say no source https //twitter com/davidlinssen/status/1640415952461529088 my best hack for prompting chatgpt is to talk like it's "baby ai " when you talk with a toddler, you start by explaining the concept and then make your point at the end if you think they grasped it so this is how you can improve your prompts ask the model what it knows about a subject > what do you know about b2b saas pricing best practices? double down on one of the items listed reiterate the key points you want the answer for > let's focus on add on pricing lease give me some best practices and strategies to implement an add on to a plg b2b saas company with an average acv of $yy continue on more items if necessary > let's focus on add on item number x final prompt > act like a pricing expert for b2b saas with 10 years of experience helping companies with pricing recommend the pricing strategies with the highest chance of improving the conversion rate from free trial user to customer consider that the add on pricing model is preferred list all the actions a pricing expert would take to improve the conversion rate from user to customer generative models explained this is the best video i found explaining how generative models work https //www youtube com/watch?v=hfiustzhs9a https //www youtube com/watch?v=hfiustzhs9a prompt engineering best practices prompt engineering is the process of designing, refining, and optimizing prompts to effectively interact with and elicit desired responses from ai language models, such as gpt 4 the main goal of prompt engineering is to improve the quality of ai generated outputs by crafting well structured, unambiguous, and context rich prompts since ai language models are trained on large datasets and learn to generate human like text, the quality of their responses can be influenced by the way users phrase their queries or statements prompt engineering involves various techniques, such as being explicit clearly specifying the desired format or type of response adding context providing relevant background information or context to help the model understand the query better repeating important information restating key points or concepts to emphasize their importance requesting step by step explanations asking the model to provide detailed explanations or reasoning behind its response experimenting with different phrasings trying different ways of asking the same question to find the most effective prompt remember llm are good at generating the next syntactically correct word they can give a false impression that the actually understand the meaning be carefull for false naratives