Let's talk AI basics - Prompt Engineering vs. Blind Prompting
Do you want to get the most out of AI? There are two main approaches: prompt engineering and blind prompting.
Prompt engineering is a technique where you carefully craft a prompt that guides the Large language models (LLM) to generate the desired output. For example, if you want the LLM to write a list of places to go with kids, you might give it a prompt like "Create a list of 10 #ai free outdoor places I can go for a day with kids in New York"
Blind prompting is a technique where you give the LLM a very general prompt and let it generate whatever it wants. For example, you might give the LLM the prompt "Things To Do In New York With Kids"
So, which approach is better?
Prompt engineering is a more reliable way to get the LLM to generate high-quality output. Blind prompting can sometimes produce surprising and interesting results, but it is also more likely to produce gibberish or unrelated content.
However, blind prompting can be a more creative process than prompt engineering. It can be fun to see what the LLM will come up with when you give it a very open-ended prompt.
The best approach for using AIs depends on your goals and preferences. If you want to be sure that your output is high-quality, then prompt engineering is the way to go. If you want to experiment and see what the LLM can do, then blind prompting can be a lot of fun.
Here's one of the great tools to supercharge your AI skills with prompt templates. https://lnkd.in/gXyFYEgj #ai #productmanagement #pm #engineering #productdevelopment
Do you want to get the most out of AI? There are two main approaches: prompt engineering and blind prompting.
Prompt engineering is a technique where you carefully craft a prompt that guides the Large language models (LLM) to generate the desired output. For example, if you want the LLM to write a list of places to go with kids, you might give it a prompt like "Create a list of 10 #ai free outdoor places I can go for a day with kids in New York"
Blind prompting is a technique where you give the LLM a very general prompt and let it generate whatever it wants. For example, you might give the LLM the prompt "Things To Do In New York With Kids"
So, which approach is better?
Prompt engineering is a more reliable way to get the LLM to generate high-quality output. Blind prompting can sometimes produce surprising and interesting results, but it is also more likely to produce gibberish or unrelated content.
However, blind prompting can be a more creative process than prompt engineering. It can be fun to see what the LLM will come up with when you give it a very open-ended prompt.
The best approach for using AIs depends on your goals and preferences. If you want to be sure that your output is high-quality, then prompt engineering is the way to go. If you want to experiment and see what the LLM can do, then blind prompting can be a lot of fun.
Here's one of the great tools to supercharge your AI skills with prompt templates. https://lnkd.in/gXyFYEgj #ai #productmanagement #pm #engineering #productdevelopment
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Product team cases where a #productteams improved content discovery
Case: Netflix and Personalized Content Recommendations
Problem: Netflix wanted to improve user engagement by enhancing content discovery and reducing churn.
Solution: Using a product outcome mindset, Netflix's product team developed a recommendation algorithm that analyzed user viewing behavior and preferences to offer personalized content suggestions.
Outcome: Netflix saw a significant increase in user engagement, with the personalized recommendations leading to higher watch times and reduced churn.
Learn more: You can read about Netflix's recommendation system in various articles and research papers, such as "Netflix Recommendations: Beyond the 5 stars" (by Netflix).
Case: Spotify and Music Discovery
Problem: Spotify users were overwhelmed by the vast music library and struggled to discover new music.
Solution: Spotify's product team used data-driven insights to create personalized playlists like "Discover Weekly" and "Release Radar," tailored to users' listening habits.
Outcome: The personalized playlists increased user engagement, time spent on the platform, and the likelihood of users discovering and enjoying new music.
Link: Learn more about Spotify's approach to music discovery in articles like "How Spotify Discover Weekly and Release Radar Playlist Work" (by The Verge).
Case: Netflix and Personalized Content Recommendations
Problem: Netflix wanted to improve user engagement by enhancing content discovery and reducing churn.
Solution: Using a product outcome mindset, Netflix's product team developed a recommendation algorithm that analyzed user viewing behavior and preferences to offer personalized content suggestions.
Outcome: Netflix saw a significant increase in user engagement, with the personalized recommendations leading to higher watch times and reduced churn.
Learn more: You can read about Netflix's recommendation system in various articles and research papers, such as "Netflix Recommendations: Beyond the 5 stars" (by Netflix).
Case: Spotify and Music Discovery
Problem: Spotify users were overwhelmed by the vast music library and struggled to discover new music.
Solution: Spotify's product team used data-driven insights to create personalized playlists like "Discover Weekly" and "Release Radar," tailored to users' listening habits.
Outcome: The personalized playlists increased user engagement, time spent on the platform, and the likelihood of users discovering and enjoying new music.
Link: Learn more about Spotify's approach to music discovery in articles like "How Spotify Discover Weekly and Release Radar Playlist Work" (by The Verge).
Here's a tip to become a great Product Manager.
Build something approach.
Find a problem, chat with 10-20 people about it, just ask questions, and organize all the feedback. I prefer to use the Opportunity Solution Tree to visualize my thinking and the best path to my desired outcome.
Now, use some cool no-code like Bubble.io, WordPress, or AI tools and create a small product yourself. Maybe even a landing page to bring the idea to life and invite some users to get to the waiting list if they see the value in your product before it even goes live.
Keep it simple and share it with the people you talked about it before and gather their thoughts again.
You can also get some users from your social media, ProductHunt, Startup Stash, etc
Repeat this process with a few more ideas! Don’t give up if people don’t see the value. It’s part of the learning process.
You will know:
- How to evaluate your product without spending a lot of time and money on it.
- How to do user interviews like a pro.
- How to test your idea or build MVP without a team of engineers.
Trust me, practice is the key. It's easier to learn by actively creating products than by just listening to podcasts or reading cool books without taking any action.
hashtag#product hashtag#productmanagement hashtag#productdevelopment hashtag#productmanager
Build something approach.
Find a problem, chat with 10-20 people about it, just ask questions, and organize all the feedback. I prefer to use the Opportunity Solution Tree to visualize my thinking and the best path to my desired outcome.
Now, use some cool no-code like Bubble.io, WordPress, or AI tools and create a small product yourself. Maybe even a landing page to bring the idea to life and invite some users to get to the waiting list if they see the value in your product before it even goes live.
Keep it simple and share it with the people you talked about it before and gather their thoughts again.
You can also get some users from your social media, ProductHunt, Startup Stash, etc
Repeat this process with a few more ideas! Don’t give up if people don’t see the value. It’s part of the learning process.
You will know:
- How to evaluate your product without spending a lot of time and money on it.
- How to do user interviews like a pro.
- How to test your idea or build MVP without a team of engineers.
Trust me, practice is the key. It's easier to learn by actively creating products than by just listening to podcasts or reading cool books without taking any action.
hashtag#product hashtag#productmanagement hashtag#productdevelopment hashtag#productmanager