Large Language Model
Definition
A type of neural network trained on vast corpora of text data, capable of generating human-like text, answering questions, summarising documents, and performing reasoning tasks. Large language models such as GPT and Claude represent significant R&D investment and are reshaping knowledge work, customer service, and content production across industries.
Complementary Terms
Concepts that frequently appear alongside Large Language Model in practice.
A branch of artificial intelligence concerned with enabling computers to understand, interpret, and generate human language. NLP powers applications such as chatbots, sentiment analysis, document classification, and automated contract review.
A business strategy that minimises investment in physical assets and instead relies heavily on intangible assets such as software, brand, data, and intellectual property to generate revenue. Asset-light companies typically exhibit higher scalability and return on capital but can be harder to value using traditional balance-sheet methods.
A business model in which a basic version of a product or service is offered free of charge while premium features, enhanced functionality, or expanded capacity are available for a subscription fee. The freemium model is prevalent in SaaS, enabling rapid user acquisition and product-led growth, with conversion rates from free to paid users typically ranging from 2% to 5%.
A dividend discount model that values a perpetual stream of cash flows growing at a constant rate, calculated as the next period's cash flow divided by the difference between the discount rate and the growth rate. The Gordon growth model is widely used to estimate terminal value in discounted cash flow analyses and requires that the assumed growth rate remains below the discount rate.
A branch of computer science focused on creating systems capable of performing tasks that typically require human intelligence, including learning, reasoning, problem-solving, perception, and natural language understanding. As an intangible asset, AI encompasses trained models, proprietary algorithms, curated training datasets, and the institutional knowledge embedded in an organisation's AI capabilities.
A subset of artificial intelligence in which algorithms improve their performance on a specific task through exposure to data, without being explicitly programmed for each scenario. Machine learning encompasses supervised learning, unsupervised learning, reinforcement learning, and deep learning techniques.
A computing architecture inspired by biological neural systems, consisting of interconnected layers of nodes that process information through weighted connections. Neural networks form the backbone of deep learning and are used in applications ranging from image recognition to financial modelling.
A technical architecture that enhances large language model outputs by retrieving relevant information from an external knowledge base before generating a response, grounding the model's output in verified, up-to-date, and domain-specific data. RAG reduces hallucination risk, enables LLMs to access proprietary or recent information not in their training data, and provides citation capabilities.
Further Reading
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