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Turn Unstructured Text Into Insights With AI
PLUS: A/B Test Between AI vs Human Content for 3 Months for SEO
Turn Unstructured Text Into Insights With AI
We have so much information today. A lot of it is in text that isn't organized. Think about podcasts, notes from meetings, handwritten notes, earnings calls, or what's on websites.
This kind of text is hard to use. It has valuable information, but it's messy and unstructured.
Unstructured Messy Text Data
Before, it was hard to get useful information from this type of text. But now, AI is making it much easier.
Untapped Potential of Messy Text
Text that isn't organized can be tough to work with. It takes a lot of time and effort to read through it and find what you need. Imagine reading hours of podcasts or going through many customer reviews. It can be slow and expensive.
But AI changes this. Large Language Models (LLMs) are a kind of AI that can organize messy text. They take that messy text and turn it into insights that is easy to consume. AI can now do this quickly and cheaply.
Unstructured Data vs Structured Data
In fact, Greg Kamradt shared that "It literally costs $0.03 to analyze a 1-hour conversation using OpenAI's GPT-4."
This is much better than the cost of paying a person to do the same work. Companies are already using this today to turn messy text into insights. Some examples include:
Otter AI: They turn meeting notes into lists of actions using transcription and summarization.
Firecrawl: They organize website text into markdown (a way to format text so its easily readable by humans) using web scrapers and AI.
Canny: They find insights in customer feedback through their bug and feature requests portal.
Otter AI
Firecrawl Dev
Canny
These companies use AI to get value from text that was hard to use before. And they do so cheaply.
1. Meta Prompting
Meta-Prompting is a pre-prompting technique which uses AI to help brainstorm a prompt.
When you use AI, you need to give it instructions (a prompt). Sometimes it's hard to know how to write these instructions. This is where the idea of a "meta prompt" can help. It's like asking the AI to help you plan your instructions.
You give the AI the text you want to work with, like a podcast transcript. Then, you ask it to suggest ways to look at the information.
Insight Extraction Prompt
For example:
"I would like to extract "insights" from the transcript below, but I'm not sure about which categories of insights I should extract. The transcript below is from a podcast called My First Million. If you were to structure insights out of this transcript, which categories would you use?"
Meta Prompt Structure
The AI will respond with ideas like:
I. Entrepreneurial & Business Insights
II. Life & Personal Development Insights
III. Social & Communication Insights
IV. Unconventional Thinking & Mindset
Nick Gray x MFM (Gemini 2.0) - Prompt #1
Now we ask it to revise the categories:
"could you make the categories more general? So they can apply to different episodes?"
Nick Gray x MFM (Gemini 2.0) - Prompt #2
Once you get the general categories, then you can ask the AI to write a prompt.
"ok, can you write me a prompt that I can put above a transcript which will instruct a language model to pull those out?"
Nick Gray x MFM (Gemini 2.0) - Prompt #3
The full conversation can be found here. If it doesn't open up, you can try the prompts on AI Studio using Gemini 2.0 itself. Its free for now.
The basic gist of Meta Prompting is to use AI to generate a prompt that you can then feed AI along with the transcript to generate insights. That's why its called Meta Prompting.
Nick Gray x MFM (Gemini 2.0) - Meta Prompt
The output of Meta Prompting is much better than normal prompts.
2. Bite-Sized Prompts
Once you have a plan, you can write your prompts. A common mistake is to ask the AI to do too much at once. This can cause problems. It's better to ask the AI to do one thing at a time.
This is called "bite-sized" prompts. Bite-Sized Prompts are small prompts with a single scope.
Bite-Sized Prompts
For example, instead of asking for several different kinds of information, ask for one thing. You might use the prompt below:
"Please find the key business ideas in this text. Include a short quote for each idea."
Nick Gray x MFM (Gemini 2.0) - Bite-Sized Prompt
It also helps to only include part of the text (first 30 minutes of the podcast), rather than the whole thing. The more context you have in your prompt, the less performance you're actually gonna get.
You can't tell a human to remember ten 10-digit numbers. Similarly, you shouldn't tell the machine to remember too much stuff otherwise it will forget the earlier stuff.
By using shorter prompts with only one focus, you make it easier for the AI to do its work, so the results are better.
3. Prompt Optimizers
Prompt Optimizers are language models specifically built for optimizing prompts.
Even the best prompts can be improved. Prompt optimizers are tools that can help with this. They let you put in your instructions and then ask for changes.
For example, the Anthropic Prompt Optimizer can help make your instructions more specific, better formatted, or include things like timestamps. You can say to the tool:
"I want this prompt to find very specific information. I want the output in markdown with bullet points and timestamps for each insight. Use actual words from the text."
Prompt Optimizers
The optimizer will then rewrite the instructions to be better. This means the AI will give you better results with more structure and timestamps.
Choosing AI Models
Different AIs are good at different things.
Arjun Kanan, CEO of ResiDesk, said, "We found that the OpenAI models tend to be the best analysts. The anthropic models tend to be the best writers and Gemini models broadly tend to be the best detectives."
For context, ResiDesk sends 1M AI chat messages per month.
The above quote was true as of 14th November 2024 when Gemini 2.0 wasn't released but now Gemini 2.0 is excellent.
Real World Example
Greg Kamradt created MFM Vault. It's a website that organizes insights from the "My First Million" podcast into actionable data.
The site uses AI to get business ideas, frameworks, quotes, and more. The structured output, along with timestamps and summaries, are all created with AI.
It shows how AI can help organize large amounts of unstructured data and create it into structured data.
Messy text contains lots of useful information. Using the right AI tools and techniques, you can turn it into something useful. Whether you need to analyze customer feedback, get ideas from podcasts, or understand information on the web, AI helps to find the signal in the world full of noise.
Hat Tip to Greg Kamradt for the prompt engineering masterclass.
Top Tweets of the day
1/
my prediction is that everyone will be a full stack engineer in 2025
Front End - v0/Replit Agent/Lovable
Back End- Cognition/Cursor Agent/Claude/ChatGPT Pro
— Adam Silverman (Hiring!) 🖇️ (@AtomSilverman)
7:01 PM • Dec 10, 2024
2025's junior engineers will need to be able to ship as much as 2020's senior engineers while doing full frontend and backend.
Thankfully, AI can 5x-10x your productivity and help you ship more in less amount of time now.
2/
Lots of wrong answers in the replies. As the former King of SMS, it’s simple:
It’s to warm the number.
You cannot send bulk texts containing a URL until your number has earned credibility with phone carriers.
To earn credibility, it needs to have replies from other numbers.… x.com/i/web/status/1…
— Nikita Bier (@nikitabier)
2:13 AM • Dec 12, 2024
TIL SMS also requires warm up mechanism just like emails.
You cannot send links in SMS before warming the numbers up just like emails.
3/
It's mind blowing to me how small the 'scene' is.
It's the same faces globally - from Art Basel to F1 to Paris Fashion Week to Ibiza to Amfar to Yacht Week to Grammys to... (list can go on)
— Lucy Guo (@lucy_guo)
8:37 PM • Dec 7, 2024
Never burn bridges. The world is a small place.
Rabbit Holes
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We A/B Tested AI vs Human Content for 3 Months for SEO by /r/SEO
The Ovarian Lottery and Other Side Projects by Breck Yunits
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