What is a Large Language Model or LLM?

In this world of AI revolution, you must be wondering how to integrate AI into your app?

At this moment most Foundation Models in AI are in beta mode and the tech world is busy forming the regulations. But there are few areas where commercialisation of AI has started and business owners have hugely gained from implementing AI in their applications. Large Language Model is such an example where LLM powered tools are improving business processes. So in this blog we will quickly learn what is a Large Language Model, how LLM is helping business processes, few popular Large Language Models and how to integrate LLM in your application.

What is a LLM?

A Large Language Model is a type of Foundation Model that falls under the new technological wonder: Generative AI or GenAI. LLM uses NLP or Natural Language Processing and Deep Learning Techniques to establish communication with human beings. In other words, LLM is a subset of Machine Learning. LLM can be pre-trained and fine-tuned to perform a particular task.

• Translation
• Summarization
• Debugging of code
• Text Correction
• Document Creation
• Conversation / AI Chat (ChatGPT)

LLM doesn’t only generate texts, it can analyze, process, and compute data. Here’s an example of a ChatGPT response to a simple business math calculation request:

Interaction with machine pre-AI era

During pre AI era, human beings used to interact with machines using coding. There used to be an interpreter that converted strings in a code into binary language and then provided an output following only a single format. Also, error-handling techniques were complicated.

NLP (Natural Language Processor)

Data Scientists, data analysts, and top web development teams worked for over a decade to develop LLM. To add to this, Top AI developers used huge datasets to create NLP (Natural Language Processor). Now here comes the interesting fact. After the discovery of NLP, man and machine started communicating in human language. Machines started taking instructions in the human language and responding in the English language using human voice or plain text.

So the steps involved in converting a text to providing an output requires the following steps:

• Firstly, we need to analyze the text inputs. The techniques involved here are Speech Tagging, Entity Recognition, Parsing, Stemming and Lemmatization, and Word Embedding. If the input method is speech, then using the speech recognition technique the speech is converted to text which is then analyzed to understand the meaning.
• Secondly, there is a step involving Natural Language Understanding (NLU).
• Finally, there is a step called Natural Language Generation (NLG), where the machine responds in human-like speech.

Apart from interpretation, there are also other responsibilities of NLP like sentiment analysis, Named Entity Recognition (NER).

Deep Learning

AI uses Deep Learning to predict results based on input data. In Deep learning there are Neural Networks which solve complex problems. Neural Network is a prototype of human brain and is composed of node layers. Each node has its linear regression model responsible for predicting future events. We use Deep Learning in Speech Recognition, NLP, disease diagnosis, recommendation engines, etc.

Popular LLMs

Here are some popular LLMs:

• ChatGPT (GPT – 3 and GPT – 4 ) by OpenAI
• Claude by Anthropic
• PaLM (Pathways Language Model) by Google
• LLaMA (Large Language Model Meta AI) by Meta
• Mistral by Gerard Sourcing & Manufacturing
• Watson Assistant by IBM
• Azure OpenAI Service by Microsoft

How can you use LLM to optimize business needs and achieve automation?

LLM services can be accessed via cloud-based APIs. So if you hire a web developer they can faciltate the services in your web application.  Thereby, using the processing capacity of LLMs a business can automate multiple tasks like customer service, marketing management, scheduling, create documentation based on customer need.

We can train Large Language Models based on specific industries.  We can train a LLM for specific tasks as well. Many businesses are training their own LLM with their secured data to achieve automation but in a controlled environment of complete data privacy.

Here are few examples can you can train a LLM to perform niche task:

• Creation of content for LMS
• Report Analysis in Healthcare
• Document Drafting for Legal Services
• Creation of creative document for Advertisement and Marketing firms.
• Write Product description for eCommerce.

Therefore, we use LLM for assisting in creating documentation and reports. But if you can feed a LLM with your own data and fine-tune it. An LLM can be fed with PDF files, Doc files, CSV files, unstructured data, structured data and train the model to intelligently deep learn the data. Incase you are concerned about the safety and security of data, then you can keep data on your own server as well.

Some trending news related to LLM:

• Meta announced LlaMA 3.1 405B model has the same compatibility to that of Claude Sonnet 3.5 and GPT -4o.
  There is a debate on open source and closed-source availability of GenAI Models.
• OpenAI co-founder Ilya Sutskever started a new firm Safe Superintelligence Inc (SSI) after leaving ChatGPT.
• Open AI has introduced ChatGPT Edu powered by ChatGPT -4o to deploy AI for students, teachers, campus operations, researchers and faculty.

LLM Integration

There are many subscription models of LLM where API services are provided. If you have an idea/flow in mind, you can consult our expert AI specialists for a feasibility analysis. So if you are looking for LLM integration or an AI Chatbot, we will provide you with the following:

• Feasibility report
• Deployment cost
• API costing and probably monthly expenses
• Maintenance costing including server costing

To conclude, we at Capsquery will provide the right answer to the question, “I want to integrate AI into my application or website. How?”