AI/ML
Large Language Model (LLM)
What a large language model is and why it matters
Definition
A large language model (LLM) is a type of artificial intelligence trained on billions of words of text to understand, generate, and reason about human language. LLMs like GPT-4, Claude, and Llama form the backbone of modern AI applications including chatbots, code assistants, document analysis, and content generation.
How it works
LLMs work by predicting the next word in a sequence. During training, they process billions of text samples and learn statistical patterns about how language works. The result is a model that can generate coherent text, answer questions, summarize documents, write code, and follow complex instructions.
The "large" in LLM refers to the number of parameters - the internal variables the model uses to make predictions. GPT-4 has over a trillion parameters. More parameters generally means better reasoning and broader knowledge, but also higher compute costs and slower inference.
For businesses, LLMs are relevant because they turn unstructured text into useful data. A customer support team can use an LLM to classify tickets automatically. A legal team can use one to extract clauses from contracts. The key is choosing the right model for the task - not every problem needs the largest model.
How 1Raft uses Large Language Model
We integrate LLMs into production applications across industries. In healthcare, we use LLMs to parse clinical notes and extract structured data. In commerce, they power product description generation and customer service agents. We always benchmark multiple models (GPT-4, Claude, open-source alternatives) against accuracy, cost, and latency before choosing one for a project.
Related terms
AI/ML
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation is a technique that combines a language model with a searchable knowledge base. Instead of relying solely on what the model learned during training, RAG retrieves relevant documents first, then generates answers grounded in that specific data.
AI/ML
Fine-Tuning
Fine-tuning is the process of training a pre-trained AI model on a smaller, domain-specific dataset to adapt its behavior for a particular task. It modifies the model's internal weights so it performs better on your specific use case without training from scratch.
AI/ML
Prompt Engineering
Prompt engineering is the practice of crafting and optimizing the instructions given to a language model to get consistent, high-quality outputs. It is the most accessible and cost-effective way to improve AI application behavior without modifying the underlying model.
AI/ML
Transformer Architecture
The transformer is the neural network architecture behind virtually all modern language models. Introduced in 2017, it uses a mechanism called self-attention to process entire sequences of text in parallel, making it far more efficient and capable than previous approaches.
AI/ML
Token (AI Context)
A token is the basic unit of text that a language model processes. Words, parts of words, and punctuation are all broken into tokens. Token counts determine model costs, context window limits, and response length constraints.
AI/ML
Model Inference
Inference is the process of using a trained AI model to generate predictions or outputs from new inputs. When you send a prompt to an LLM and get a response, that is inference. It is where compute costs, latency, and user experience are determined.
AI/ML
AI Hallucination
AI hallucination is when a language model generates information that sounds plausible but is factually incorrect or entirely fabricated. It is a fundamental behavior of LLMs, not a bug, and managing it is essential for any production AI application.
Related services
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