Generative AI

Large Language Models

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A large language model (LLM) is a branch of artificial intelligence (AI) that uses deep learning and neural networks to understand and mimic written and spoken words. LLMs can recognize, summarize, and create text and other forms of content.

Why Are Large Language Models Important?

LLMs are trained using large volumes of data to uncover patterns and relationships between words and phrases to mimic human intelligence. The value derived from the development of LLMs allows humans to feel naturally confident in verbal and chat-based conversations with applications. A deep understanding of language enables LLMs to perform almost error-free translation and transform documents and voice recordings.

Applications of Large Language Models

Below are examples of LLM use cases helping businesses today:


Chatbots are conversational user interfaces that engage web visitors and customers with service requests. Chatbots are helpful because they provide users with instant access to a persona that understands customers’ common challenges, can provide them with workarounds and sales information, and can arrange follow-up calls with human agents. Chatbots make the vendor appear more responsive and eager to help, without overloading agents with trivial questions.

Content Generation

One benefit of LLMs for content creation is that they write grammatically correct text for websites, blogs, and knowledge-based articles, reducing effort for human copywriters who review and edit the generated content rather than write it from scratch.


Translating websites and catalogs from one language to another is time-consuming and error prone. LLMs ease this task thanks to their deep understanding of language, syntax, and structure.

Social Media Analysis

A business can better understand how customers perceive their brand and products, and gauge their reactions to news. LLMs can analyze social media feeds to extract the gist or sentiment and use it as actionable feedback for future positioning.

Summarization and Transcription

LLMs can summarize articles, web recordings, and audio meetings to create concise notes. Action points can be highlighted to make follow-up meetings more productive.

Text Analysis

Conversational LLMs can be used to probe the content of a large body of text interactively. Relevant sections can be pulled out based on the questions being asked.

Speech Analysis

LLMs can improve machine interactions using speech recognition. For example, you can tell your smart thermostat you feel cold, which initiates the heating system to increase the temperature. This is possible because the LLM understands the context of your statement.

Training a Large Language Model

The following steps are needed to train an LLM:

Collecting Data

An LLM can be improved with a large volume of relevant data to learn the lexicon of the business area it will be supporting.


The collected data must be preprocessed to make searching easier by delimiting keywords, improving formatting, and filtering out unrelated data that could mislead the LLM.

Selecting an Architecture

An architecture must be selected. This can be a Generative Pre-trained Transformer (GPT) series, Bidirectional Encoder Representations from Transformers (BERT), PalM, Large Language Model Meta AI (LLaMA), or Falcon.


The goal of pretraining is to enable the LLM to understand language usage in a specific domain so the customer feels confident when interacting with a service bot.


To fine-tune the model, supervised learning is helpful to ensure the best answers are tagged or labeled.

The Benefits of Using Large Language Models

Using LLM has many benefits for a business or government service, including the following:

  • Enhanced productivity: There is so much text and video content to search for businesses to educate themselves on their market and how they are perceived that LLMs offer the most productive way to follow market trends.
  • Improved customer service: Customers use chatbots when they see them as responsive and helpful.
  • Reputational protection: Local governments and businesses need to understand how they are seen by the public and customers. LLMs can analyze social media and surveys to summarize the feedback from policy, outreach, and PR actions.

How the Actian Data Platform Can Help

The Actian Data Platform makes it easy to automate data preprocessing as part of your LLM training workflow thanks to its built-in data integration capabilities. Businesses can proactively preprocess their operational and social media data to always be analysis-ready using pipeline automation.

Actian provides the easiest data platform solution on the market. See for yourself by starting a free trial.