Unleashing the Power of LLMs: An Effixis Guide - Part 02

Beyond Text: Unleashing the Power of LLMs in Search, Clustering, and Classification


ai generated image to accompany the blog
Courtesy: DALL·E 2

In the ever-evolving landscape of AI-driven natural language processing, we have embarked on a two-part journey to unravel the multiple applications of Large Language Models (LLMs). In the first part, we explored the world of Prompt Engineering, where LLMs proved their power in generating human-like text.

Now, in the second and last part of our blog, we shift our focus to the impact LLMs have in the scope of Search and Similarity Analysis, Data Clustering, and Data Classification. These applications go beyond simple keyword searches. They analyze and understand the meaning of words, group similar ideas, and arrange data efficiently.

Effixis encourages you to look at practical uses of LLMs. We invite you to see their potential, be creative, and find out the many ways that AI-powered language models can be useful.

In case you missed out, check out the Part 01 here →

Use Cases

  1. Search and Similarity Analysis 

In the world of data, the ability to effectively search and analyze for similarities is a vital asset. LLMs are instrumental in this endeavor, having the ability to delve into extensive data sets and retrieve specific information or identify related content. By using text embedding they are able to not only use keyword matching but also semantic similarities, called semantic similarities.

Prompt designed to find similar academic articles in a database

Consider prompting a LLM the following:

By analyzing the given article, the LLM can identify similar articles in the database based on the topic, research methodology, findings, or even writing style.

There are countless potential applications for LLMs in Search and Similarity Analysis, including:

  • Searching historical data for similar market trends
  • Analyzing literary works to find those with similar themes or writing styles
  • Identifying similar medical case studies for better diagnosis
  • Analyzing code repositories to find similar algorithms or methods
  • Locating customer reviews that mention specific product features

In our experience at Effixis, the application of LLMs in Search and Similarity Analysis has been transformative, allowing us to monitor and identify market trends →.

2. Data Clustering

Data Clustering is a powerful application of Large Language Models (LLMs), where these models are tasked with arranging similar data into distinct clusters. This aids in better data visualization and understanding.

Consider prompting a LLM the following :

Prompt designed to cluster customer feedbacks

By processing the customer feedback, the LLM can cluster the data into groups like positive, negative, or neutral, providing a clear sentiment analysis.

Potential applications of LLMs in Data Clustering are varied and extensive, for instance:

  • Categorizing blog posts based on their content
  • Segmenting customers based on purchasing behavior
  • Grouping customers by their interaction with a website
  • Organizing product inventory based on product attributes
  • Clustering health records based on patients’ symptoms

At Effixis, we used data clustering to cluster patients based on several characteristics →

3. Data Classification

Another compelling use case for Large Language Models (LLMs) is Data Classification. In this scenario, LLMs are leveraged to assign predefined categories (as opposed to clustering) to various pieces of data, aiding in efficient organization and retrieval.

Consider prompting a LLM the following:

Prompt designed to classify user’s intent based on social media posts.

By interpreting the language and sentiment of the posts, the LLM can classify them according to user intent, providing valuable insights for the social media team.

Data Classification applications with LLMs are vast and versatile. Some other examples include:

  • Classifying emails into folders based on their content
  • Categorizing scientific literature by research fields
  • Classifying job applications according to job roles
  • Classifying survey responses based on respondent demographics
  • Classifying financial transactions based on their types

At Effixis, we used data classification to classify artists in several categories →

As we’ve seen, understanding the fundamentals of Large Language Models, the art of Prompt Engineering, and the power of Text Embedding lays a strong foundation for harnessing their capabilities effectively. With practical examples from Effixis illuminating each use case, we hope this article has provided you with valuable insights into the limitless possibilities these models offer.

Embrace the potential, explore the creativity, and continue to unlock new horizons in the realm of AI-driven natural language processing. Do not hesitate to reach out and schedule a call to learn more →


Basile Midrez

Studying Business Engineering at UCLouvain, Basile has cultivated a deep affinity for start-ups and innovative technologies. During his internship at Effixis, he supported the marketing team in content creation and analysis.


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