AI Experiments – Case Study

Generative AI based on Large Language Models (LLMs) such as ChatGPT and Google NLP generate generic content. For generating personalized content, LLMs have to be trained on vast amounts of data to capture patterns and relationships within the language and tonality. However, generating personalized outputs requires intense computational resources. The data can be over millions of tokens, so there was a need to reduce the cost.

The AIAV twitter bot

AIAV is a context engine built on top of ChatGPT that allows users to create personalized content (bios, raps and answers) based on their social media persona only at a fraction of a cost of using LLMs

Features of AIAV:
  • Identifying relevant data from a source such as individual tweets.
  • Feeding data to the LLMs that is relevant to a particular individual.
  • Producing personalized outputs such as raps, poems, love letters, dating profiles and so on.
  • Easily able to mimic famous personalities and individuals.

User Reactions

The Use-Cases

A persona of a judge/lawyer can be developed, giving you far greater insights on where a particular case would go.

Doctors’ personas can be developed and the AI can give second opinions based on persona by looking at reports, and the history of a patient and doctor.

By looking at your emails it can give you clear insights into what you say and what you do. It can even become your assistant based on you

Get a financial research assistant with instant access to numbers.

An eCommerce store can personalize what marketing material and products to sell based on who you are.

Automate 90% of the mundane tasks in HR such as reading and scoring resumes, creating job descriptions and shortlisting the best candidates.

The Results

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