Editorial & Advertiser disclosure

Global Banking and Finance Review is an online platform offering news, analysis, and opinion on the latest trends, developments, and innovations in the banking and finance industry worldwide. The platform covers a diverse range of topics, including banking, insurance, investment, wealth management, fintech, and regulatory issues. The website publishes news, press releases, opinion and advertorials on various financial organizations, products and services which are commissioned from various Companies, Organizations, PR agencies, Bloggers etc. These commissioned articles are commercial in nature. This is not to be considered as financial advice and should be considered only for information purposes. It does not reflect the views or opinion of our website and is not to be considered an endorsement or a recommendation. We cannot guarantee the accuracy or applicability of any information provided with respect to your individual or personal circumstances. Please seek Professional advice from a qualified professional before making any financial decisions. We link to various third-party websites, affiliate sales networks, and to our advertising partners websites. When you view or click on certain links available on our articles, our partners may compensate us for displaying the content to you or make a purchase or fill a form. This will not incur any additional charges to you. To make things simpler for you to identity or distinguish advertised or sponsored articles or links, you may consider all articles or links hosted on our site as a commercial article placement. We will not be responsible for any loss you may suffer as a result of any omission or inaccuracy on the website.

Home > Technology > Unravelling the potential of large language models

Unravelling the potential of large language models

Published by Jessica Weisman-Pitts

Posted on September 21, 2023

5 min read

Last updated: January 31, 2026

Illustration of large language models in AI and NLP technology - Global Banking & Finance Review — This image visually represents the concept of large language models (LLMs) in artificial intelligence. It highlights their significance in natural language processing (NLP) and their transformative potential in various applications, aligning with the article's focus on understanding LLMs.

Unravelling the potential of large language models

Uncovering the Secrets Behind Their Success

By Henry Vaage Iversen, CCO & Co-Founder, Boost.ai

The world of natural language processing (NLP) and artificial intelligence (AI) has undergone a monumental transformation in recent years thanks to the advent of large language models (LLMs). These sophisticated models, like OpenAI’s ChatGPT, have opened up new avenues of opportunity with their ability to grasp human-like text and excel at various language-related tasks. The potential of this groundbreaking technology has captivated people worldwide, promising to revolutionise our lives and work. However, understanding the functionality of LLMs is vital to realising their full potential.

LLMs’ core functionality lies in a neural network trained on vast textual datasets. By scrutinising patterns and relationships within the data, they become adept at predicting the next word in a sentence. Through this process, LLMs gain an innate understanding of grammar, syntax, and even subtle semantic nuances, allowing them to generate coherent and contextually appropriate responses when given prompts or queries.

The training regimen involves exposing the model to vast quantities of data, including books, articles, and websites. The model becomes adept at identifying patterns, extracting meaning, and generating text based on the input it receives. Consequently, LLMs possess an astonishing ability to emulate human language, holding the potential to elevate a wide range of applications and services.

Implementing Large Language Models Successfully

While the potential of large language models is enormous, their successful implementation necessitates careful adoption. Here are some essential factors to ensure effective deployment:

Adapting LLMs for your needs: Fine-tuning LLMs for specific applications is a game-changer for boosting performance. This process involves training the model on task-specific data and adapting it to a particular domain or problem. The result is an optimised output for specific use cases, such as customer support or content generation.

Building user feedback loops: Creating successful LLM implementations requires a feedback loop from users. Collecting feedback, monitoring user interactions, and iteratively improving the model based on this feedback are critical steps to refine its performance over time.

Accounting for bias: The bedrock of training LLMs lies in high-quality data. Curating diverse and representative datasets becomes paramount to mitigate biases and prejudices in the source data. Moreover, continuous monitoring and evaluation are indispensable to identify and rectify potential biases during the model’s deployment.

Leveraging Large Language Models for Exceptional Virtual Agents

One area where LLMs have showcased remarkable potential is their integration with conversational AI. By combining LLMs with Natural Language Understanding (NLU), a hybrid system emerges, harnessing both technologies’ strengths. While NLU delivers precise and reliable responses within a specific business context, LLMs optimise content generation with their vast general knowledge. This combination of technologies can also improve virtual agents in a few other key ways too:

Training AI properly: The text generation prowess of LLMs can streamline the AI training process. They can generate on-brand virtual agent responses, provide multiple alternatives for a single sentence to use as training data and rewrite answers to match specific lengths and tones, saving days of manual work.

Providing expanded language capability: LLMs can also be invaluable in expanding virtual agent solutions into new markets or languages. They offer translations and suggest synonyms when building out a language-understanding dictionary, saving time and enhancing the quality of the virtual agent.

Creating better customer service: Customer service agents can harness LLMs to enhance their capabilities. LLMs can scan and summarise conversations between customers and virtual agents, providing human agents with clear and concise insights. Additionally, they help fine-tune the length and tone of responses to match the conversation context and generate pre-written messages incorporating essential information from the conversation history.

Why a Hybrid Approach Holds the Key to Success

With such a diverse range of benefits and capabilities, it would be easy to think LLMs have no downside. However, responsible and well-considered adoption is paramount. The raw processing power of LLMs is extraordinary, but they can be susceptible to hallucinations and inaccuracies. Embracing a hybrid approach is the way to go if businesses seek to deliver the best customer-facing virtual agent experiences. Connecting LLMs to conversational AI pre-trained on company-specific data, with appropriate guardrails in place, allows for virtual agent scalability and creativity without compromising accuracy and data quality.

We have entered the age of the LLM and, with it, a new age of technological efficiency. By understanding the strengths and weaknesses of this technology, as well as having an understanding of our specific needs, businesses can unlock the full potential of LLMs to enrich user experiences and create more intelligent and engaging conversational systems. Nevertheless, it remains crucial to address biases and connect these models to reliable source data to ensure the information they provide is accurate, thus preventing their potential in an enterprise setting from going unrealised.

Unravelling the potential of large language models

Unravelling the potential of large language models

Implementing Large Language Models Successfully

Leveraging Large Language Models for Exceptional Virtual Agents

Why a Hybrid Approach Holds the Key to Success

Frequently Asked Questions about Unravelling the potential of large language models