New Custom GPT: Find Keywords – A Powerful Tool for Content Analysis

New Custom GPT: Find Keywords

I’ve created a new Custom GPT that will reduce text to just the keywords. The GPT is named Find Keywords and can be found at

Note that it requires a subscription to ChatGPT Plus. Unfortunately, OpenAI does not offer trial subscriptions, and subscriptions cost $20 USD per month. Personally, I find the subscription well worth it for both personal programming projects and for personal productivity.

What Does This GPT Do?

This GPT, named Find Keywords, specializes in processing text to extract and list keywords in a concise, alphabetized format. It eliminates duplicates, incorporates abbreviations where applicable, and presents the keywords in title case. If URLs are provided, they are listed separately under a “Links” heading with their titles and URLs. This tool is designed to help users identify the key concepts or terms within a given text or list, making it easier to understand or summarize the main points.

What’s a Good Usage of This Custom GPT?

A good usage of the Find Keywords custom GPT, is to process large amounts of text to extract and organize the most important words or phrases into a concise, alphabetically sorted list. This can be particularly useful for:

  • Academic research, to quickly identify the main topics or keywords within papers, articles, or notes.
  • SEO (Search Engine Optimization) purposes, to find key terms that could improve the visibility of web content in search engine results.
  • Content analysis, to understand the primary focus areas of documents, speeches, or social media posts.
  • Summarization tasks, where identifying keywords can help in understanding the main points of lengthy texts without reading them in full.

By providing text to Find Keywords, users can save time and effort in distilling the essence of content, making it easier to categorize, analyze, or utilize for further research or content creation tasks.

Why I Created This

I rely heavily on Google Keep for notes and to-do lists, but it had been feeling sluggish, especially on my phone. To streamline things, I decided to transfer longer notes to Google Docs (a fantastic feature in Keep!). However, I still wanted the ability to quickly search for old notes using key terms.

That’s where the Find Keywords custom GPT comes in handy. Here’s my process:

  1. Copy and paste the text of a lengthy Keep note into the GPT.
  2. Process the text through the GPT to get a condensed output.
  3. Transfer the original note to Google Docs, preserving the Docs URL within the Keep note.
  4. Paste the GPT’s output back into the Keep note.

Now, I can still easily search for keywords from the original note, even though the full version lives in Docs!

For example, here’s the GPT output from a webinar I attended earlier this week. Notice how it maintains searchability for key concepts from my notes, despite being much shorter.

Keywords: AI Makerspace, Challenges, Coding, Community, Computing Power, Data Analytics NYC, Data Parallelism, Data Scientist, Deb Nicholson, Discord, Distributed Computing Framework, Evening, Fine Arts Major, General-Purpose Framework, GitHub, History, Introduction, Large Language Models (LLMs), Lily Su, Machine Learning, Meetups, Model Parallelism, Parallelization Techniques, Parallelize, Pittsburgh, PyCon 2024, Python, Python Coding,, Ray, Ray Project, Scalable, Session, T-Mobile, TensorFlow, Training, YouTube, YouTube Live Events

An Evening of Python Coding –
Today’s session –
History of Meetups –
AI Makerspace YouTube Channel –
AI Makerspace Discord –
PyCon 2024 –
Lily Su’s Website –
Lily Su on GitHub –
Google Colab Notebook 1 –
Google Colab Notebook 2 –
Session Recording –

My Other Custom GPTs

You can read about all my custom GPTs here.

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