Product-Market Fit is a term used to describe when the offering a business has meets a market of customers. Most founders will never truly achieve it, instead, they get close and call it good enough - this is the “Product-Market Squint”.

Let’s examine the original thoughts and how to adapt them to today.

I first wrote that on March 30, 2017. Has much changed in how founders seek out and attain Product-Market Fit in the age of AI?

While it’s become easier than ever to discover what people think and care about (we share everything with immense volume on multiple platforms), it’s still, arguably, pinning down what people will pay for is as slippery as ever.

Make use of LLMs to scour Reddit, sites, and more to do an early pass on user research before committing more of your time to design and code.

SYSTEM
You are “ResearchCopilot,” a UX strategist.

USER
Here is a raw scrape from r/[Subreddit] discussing problems with [domain] (≈3,000 words).  

1. Extract the 12 most common pain points as bullet phrases (≤10 words each).  

2. Group those pains into up to 4 Jobs-to-Be-Done buckets.  

3. For each bucket, list one verbatim quote (<120 chars) that exemplifies it.  

[[PASTE THREAD TEXT]]

An MVP remains a moving target, unlocking value only momentarily until that value has been consumed, internalized, and normalized.

One-shot prompts and AI IDEs empower founders, product managers, and others to engage with customers more frequently without incurring the cost or risk.

SYSTEM  
You are “MVP-Smith,” an expert in lean TypeScript + React prototypes.

USER  
Draft a minimal **React + Express** project that proves this value hypothesis:  

“Users will upload a PDF and receive a one-sentence summary.”
  
Constraints:  
• Front end = one drag-and-drop zone + spinner + result box.  
• Back end = single `/summarize` endpoint calling OpenAI API (gpt-4o).  
• Include `package.json`, env variable placeholders, and comments for TODOs.  

Respond with the full folder tree and inline code blocks.

Your unfair advantage is still worth playing whenever you can. Much like a bottom-up TAM is preferred to a top-down one, insider knowledge lets you leverage not just the problem but the players.

AI gives everyone their local expert to learn from. The real advantage will come from building up your own fine-tuned data to yield even better results.

SYSTEM  
You are “FineTune-Maker,” skilled at converting domain docs into Q-A pairs.

USER  
Here is an internal FAQ for our niche industry (≈2,000 words).  
Turn it into **50 JSONL** training lines.  
• `prompt`: a likely question from a newbie operator (≤120 chars)  
• `completion`: the expert answer (≤250 chars, no code fences)  
Output JSONL only—one line per pair—ready for OpenAI fine-tuning CLI.  
  
[[PASTE FAQ]]

PMF is still measured through metrics like revenue, engagement, and more, still proxies of the value and sentiment being derived.

AI lets us look at data with new eyes, spotting the patterns that were previously difficult or expensive to surface. Further, AI can engage our customers directly via chat and features to capture new sets of data on sentiment, NPS, and more.

SYSTEM  
You are “RetentionRadar,” a data-savvy product analyst.

USER  
I’ve pasted a CSV with daily active users, sign-up date, plan tier, and churn flag.  
1. Identify the three cohorts with the **highest early churn (≤30 days)** and quantify churn %.  
2. Suggest one hypothesis per cohort for why they leave.  
3. Recommend one experiment each to improve retention.  
  
[[PASTE CSV]]

This post is a part of the “Just in Time” series, where I revisit and adapt my experiences and advice for founders from the past 20 years. It is based on “Product-Market Squint”, originally published on March 30, 2017.