What's an LLM and How Do LLMs Work?

Large Language Models (LLMs) have transformed artificial intelligence in the past years, fundamentally changing business operations. The release of ChatGPT by OpenAI in late 2022 sparked widespread adoption and awareness of LLMs.

Following this breakthrough, tech giants Microsoft, Google, and Anthropic launched their own LLM solutions, further driving adoption. These tools proved especially valuable for business applications due to their ability to handle complex language tasks. Today, companies are increasingly implementing LLMs to automate customer service, generate content, and analyze data—streamlining operations, cutting costs, and unlocking new opportunities for innovation.

This introductory article will explain what LLMs are, how they function, and crucially, how businesses can harness them to boost growth and efficiency.

Large Language Models (LLMs) are a specialized type of artificial intelligence that uses deep learning to process and generate human-like text. They can perform a wide range of tasks—from generating code and solving mathematical problems to processing structured data, creating image prompts, composing music, generating algorithms, describing charts, simulating game environments, and interacting with robotic systems.

As a subset of machine learning, they are built on the transformer architecture - a neural network design specifically created for processing sequential data like text. Deep learning, which powers LLMs, works by using multiple layers of artificial neurons that progressively learn more complex features from the input data, similar to how human brains process information.

These models are trained on massive datasets collected from various sources including books, websites, academic papers, and social media posts. During training, they learn patterns in language by analyzing billions of text examples. The training process happens in two main stages: pre-training, where the model learns general language understanding from this vast dataset, and fine-tuning, where it's specialized for specific tasks using smaller, more focused datasets.

Additionally, LLMs can be customized through prompt-tuning, a technique where specific instructions or examples guide the model to perform particular tasks without modifying its underlying parameters. This flexibility allows LLMs to adapt to various applications while maintaining their core capabilities.

LLMs represent a specialized subcategory of artificial intelligence focused on natural language processing. Unlike traditional rule-based systems, LLMs use deep learning techniques to understand and process language in a more nuanced and context-aware manner. They form part of the broader AI ecosystem, they can complement other technologies like computer vision, robotics, and expert systems.

Here are its main components of artificial intelligence:

• Machine learning (ML): A subset of AI that enables systems to learn from data

  • Deep Learning: A specialized form of ML using neural networks

    • Large Language Models: Specialized deep learning models focused on natural language processing tasks

• Computer vision: Systems that can understand and process visual information

• Robotics: Physical systems that can interact with the environment

• Expert systems: Rule-based AI systems for specific domains

LLMs have become increasingly vital for businesses. Today's leading LLMs—including OpenAI's GPT-4, Google's PaLM, Anthropic's Claude, and Meta's LLaMA—are transforming how companies handle language-intensive tasks. These models enhance customer service through sophisticated chatbots providing 24/7 support, streamline content creation and analysis, and efficiently process vast amounts of information.

A crucial factor in maximizing LLM capabilities is prompt engineering—the art of creating well-structured queries or instructions that guide these models to produce desired outputs. As a subset of generative AI, LLMs create new content based on learned patterns, but their effectiveness depends heavily on well-crafted prompts. Skilled prompt engineering helps businesses unlock these models' full potential while maintaining output quality and relevance.

The impact of LLMs extends across multiple business sectors. Healthcare organizations streamline operations by using LLMs to process patient records, interpret medical research, and support clinical decision-making. In finance, these models improve efficiency through market analysis, automated risk assessment, and fraud detection. Law firms and legal departments leverage LLMs to expedite document review, simplify contract analysis, and accelerate legal research.

These are just a few applications that demonstrate how LLMs can transform business operations across different industries.

Large language models operate through a sophisticated layered architecture that combines multiple machine learning technologies.

At their core, they leverage neural networks, which serve as the foundation for deep learning models that extract complex patterns from data. Transformers, the standard architecture for LLMs, build upon these networks with specific innovations that allow for advanced language understanding.

Key components of the transformer architecture include:

• Pre-training: LLMs learn language patterns from vast text datasets, capturing contextual relationships between words and phrases.

• Attention mechanisms: Self-attention enables models to focus on the most relevant parts of the input text, improving contextual understanding and response accuracy.

• Tokenization: Text is divided into smaller units (tokens) for systematic processing and generation.

• Scalability: Transformers' parallel processing capabilities allow them to handle massive datasets efficiently.

This combination enables LLMs to generate human-like responses with remarkable accuracy and contextual awareness.

As LLMs continue to evolve, they are transforming how businesses tackle operational challenges. Here are three main categories of applications with examples:

• Predictive analytics

  • Risk assessment: LLMs analyze complex datasets to identify potential risks in business operations, financial markets, and cybersecurity

  • Market trend analysis: These models process vast amounts of market data to identify emerging patterns and opportunities

• Generative AI

  • Content creation: LLMs can produce high-quality articles, marketing copy, and social media content

  • Code generation: Developers can leverage LLMs to automate routine coding tasks and accelerate software development cycles

  • Response generation for customer service: These models power intelligent chatbots that provide personalized, context-aware customer support

• Natural language processing (NLP)

  • Data analysis and summarization: LLMs can process and distill insights from large volumes of unstructured text data, making information more accessible

  • Research synthesis: These models can analyze multiple sources to provide research summaries and identify key findings

LLMs offer several advantages for businesses that can transform their operations and drive competitive advantage:

Scalability and broad applications: LLMs can handle an increasing volume of tasks without proportional increases in resources. For example, a customer service department can handle thousands of simultaneous inquiries using LLM-powered chatbots, effectively scaling their support capabilities without adding staff.

Efficiency and automation: Processing and analyzing vast amounts of text data quickly. A legal firm, for instance, can use LLMs to review thousands of documents in hours rather than weeks, significantly reducing manual review time and costs.

Versatility in task handling: From content creation to data analysis. Marketing teams for example, can use them to generate multiple versions of ad copy, while research teams can use LLMs to summarize extensive market reports.

Consistent performance: LLMs maintain consistent quality and availability 24/7, ensuring reliable service delivery. This is particularly valuable for global businesses operating across different time zones or those requiring round-the-clock service.

However, businesses should be aware of several important limitations when implementing LLMs:

Development and operational costs: Implementing LLM solutions requires significant investment. Training custom models or using commercial APIs can be expensive, especially for small to medium-sized businesses. For example, a company processing 1 million customer queries monthly through GPT-4 could face costs exceeding $20,000, making it crucial to carefully evaluate the return on investment.

Data security and privacy concerns: When processing sensitive business information through LLMs, there are inherent risks related to data privacy and security. Customer information, proprietary data, or confidential business strategies processed through these models could potentially be exposed or compromised. Healthcare organizations, for instance, must ensure their LLM implementations comply with HIPAA regulations when handling patient data.

Accuracy and hallucinations: LLMs can sometimes generate plausible-sounding but incorrect information, known as hallucinations. This is particularly problematic in business contexts where accuracy is crucial. For example, in financial services, an LLM providing inaccurate investment advice or misinterpreting market data could lead to significant losses and liability issues.

Training data bias: Models can inherit biases present in their training data, potentially leading to unfair or discriminatory outputs. This could affect hiring processes, customer service interactions, or market analysis, potentially exposing businesses to reputational damage or legal risks. For instance, an LLM used in recruitment might show unconscious bias in how it evaluates candidate responses.

Here's a summary of the advantages and limitations of LLMs:

Integrating LLMs into business applications has become more accessible through platforms like Appsmith, an open-source low-code AI development platform designed for building custom applications quickly and securely.

Appsmith AI enables businesses to build AI-powered applications by creating custom interfaces and connecting to various LLMs—including OpenAI, Google AI, and Anthropic—while integrating with multiple data sources.

You can build advanced copilot experiences by creating sophisticated multi-turn conversations that combine LLM chat functionality with custom JS objects and data. For example, you could develop a customer service copilot that accesses your product database to answer inventory questions while maintaining conversation context and providing personalized recommendations based on customer purchase history.

Appsmith AI offers a comprehensive suite of AI actions, including text processing capabilities for content generation, classification, summarization, and information extraction. It also provides image processing features that can classify images, generate descriptions, and extract relevant information from visual content.

Low-code AI platforms are increasingly gaining popularity as they democratize AI development by enabling both technical teams and citizen developers to easily build and ship AI-powered apps without extensive coding experience, accelerating development and making long-term maintenance more straightforward.

Key benefits of low-code AI development include:

• Faster development cycles: Build and deploy AI applications in days rather than months.

• Cost-effective implementation: Reduce development costs by minimizing the need for specialized AI developers.

• Simplified integration: Easy-to-use visual interfaces and pre-built connectors make it simple to integrate AI capabilities into existing business applications.

• Scalable solutions: Low-code AI platforms provide the infrastructure needed to scale AI applications as your business grows.

With Appsmith, developers can build any application and seamlessly connect it to their existing datasources, including SalesForce, Hubspot, MySQL, or any REST API. Offering over 40 pre-built widgets to accelerate application development and implement business logic using JavaScript for maximum flexibility and customization.

You can get started quickly by choosing either cloud deployment or self-hosting options. If you need help integrating AI into your applications, talk to our team about your custom requirements or request a demo