1. Sentiment Analysis
2. Content Moderation
3. ChatBot by integrating Custom Data
4. Language Translation
5. Health Care - Diagnosis Example
More than 150 examples across various LLMs.
Large Language Models (LLMs) are advanced neural networks designed to understand and generate human-like text based on vast amounts of data. They leverage deep learning techniques to process natural language, enabling applications in translation, summarization, and conversational agents, among others. Prominent examples include OpenAI's GPT series, Google's BERT, and Meta's LLaMA models.
1. ChatGPT 4o
2. Anthropic Claude 3
3. Google Gemini 1.5
...
Retrieval Augmented Generation (RAG) combines the capabilities of retrieval-based models and generative models to improve the accuracy and relevance of generated content. In this approach, relevant documents or information are retrieved from an external database and then used as context for the generative model to produce more informed and accurate responses. This technique is particularly useful for tasks requiring detailed and specific knowledge, such as question answering and summarization.
Diagnosis Service uses Gen AI with Large Language Models and RAG (Retrieval Augmented Generation) to summarise the patient diagnosis data for the past years.
Sentiment Analysis done on Custom Data (Movie Reviews). Examples of Neutral, Positive and Negative Sentiments. The answers were structured with Rating, the feeling
Content Moderation can be tuned and used for various business applications where the App needs to interact with the user.
Extract Number, Date, Time and Models from free flowing text data.
Conversation with a Customer Service Agent with the knowledge of Car Rental Service.
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