What is an LLM?

Summary

• A large language model (LLM) is a transformer-based neural network trained on vast text data to generate, summarize, translate, and analyze language using next-token prediction.
• LLMs are trained through pre-training on trillions of tokens, fine-tuning on domain-specific data, and alignment techniques like RLHF, but they carry risks such as hallucinations, bias, and lack of business context.
• Databricks helps enterprises move from raw LLMs to production-ready AI agents through Agent Bricks, providing unified governance, contextual reasoning over enterprise data, and self-improving evaluation pipelines.

What is an LLM? A guide to large language models

Every time you ask a chatbot a question or use AI to draft an email, a large language model is doing the work behind the scenes. Understanding what large language models are, how they function, and where they fall short is essential for any organization exploring AI. According to McKinsey, 78% of organizations now report using AI in at least one business function, up from 55% just a year earlier.
A large language model (LLM) is a neural network trained on vast amounts of text for natural language processing tasks. LLMs can generate, summarize, translate, and analyze text across many contexts. Trained with millions or billions of parameters, they power modern chatbots, generative AI applications, and AI agents.

How do LLMs actually work?

An LLM's primary objective is to predict the next token in a sequence. This enables it to produce continuous, context-aware text. The process follows a clear pipeline:

1. Tokenization: Text is broken into smaller units called tokens, words, subwords, or individual characters.
2. Embedding: Each token is mapped to a vector that captures semantic meaning. Tokens with similar usage sit close together in a high-dimensional space.
3. Self-attention: A core mechanism in transformers, self-attention weighs the importance of every token relative to the others. This improves long-range context understanding.
4. Generation: The model outputs text one token at a time. It calculates probabilities for all potential next tokens and selects the most likely one.

Repeating this cycle millions of times produces full paragraphs, code blocks, or translations.

Key components of LLM architecture

Modern LLMs share a common structural blueprint:

Component	Role
Input embedding layer	Converts raw tokens into dense numerical vectors
Stacked transformer blocks	Apply self-attention and feed-forward operations to learn contextual relationships
Output layer	Computes a probability distribution over the vocabulary for the next token

Most generative LLMs use a decoder-only transformer design, first outlined in the 2017 paper "Attention Is All You Need."

LLMs vs. traditional NLP models

Traditional NLP models such as RNNs and LSTMs process text sequentially. They struggle with long-range dependencies and scale poorly. LLMs differ in several key ways:

• Parallel processing: Transformers handle entire sequences at once, dramatically reducing training time.
• Scale: LLMs contain billions of parameters, compared to millions in older architectures.
• Generalization: Pre-training on diverse corpora lets LLMs perform well on tasks they were never explicitly taught.

These differences explain why LLMs have largely replaced earlier approaches for complex language tasks.

How LLMs are trained

Training an LLM involves three broad stages:

1. Pre-training: The model learns general language patterns by predicting masked or next tokens across trillions of words from books, websites, and code repositories.
2. Fine-tuning: A pre-trained model is further trained on a smaller, domain-specific dataset. This specializes it for tasks like medical summarization or legal analysis.
3. Alignment: Techniques such as reinforcement learning from human feedback (RLHF) steer outputs toward helpfulness, accuracy, and safety.

Each stage adjusts billions of parameters using hyperparameters like learning rate, batch size, and number of epochs.

Limitations and risks of LLMs

LLMs are powerful but imperfect. Teams should plan for these known challenges:

• Hallucinations: The model generates plausible-sounding but false information.
• Bias amplification: Training data biases can surface in model outputs, creating fairness and compliance risks.
• Missing business context: General-purpose LLMs lack proprietary enterprise knowledge. This causes agents to retrieve incorrect information or overlook critical documents.
• Lack of explainability: It is difficult to trace why a model produced a specific answer.
• Environmental cost: Training and serving large models requires significant compute and energy.

Without systematic evaluation, most teams rely on ad-hoc spot-checks, methods too slow and inconsistent to catch critical failures.

Common enterprise use cases for LLMs

Organizations apply LLMs across a wide range of functions:

• Customer support: Chatbots that resolve questions using internal knowledge bases.
• Content generation: Drafting marketing copy, reports, or documentation at scale.
• Information extraction: Pulling structured data from contracts, invoices, or research papers.
• Code assistance: Autocompleting, reviewing, or explaining code for development teams.
• Multi-agent workflows: Orchestrating multiple AI agents that collaborate across enterprise functions.

How developers integrate LLMs into applications

Developers typically access LLMs through APIs or model-serving endpoints. Key considerations include:

• Model selection: Choose between open-weight models (e.g., Llama) and proprietary APIs (e.g., OpenAI, Anthropic) based on cost, latency, and data-privacy requirements.
• Prompt engineering: Craft instructions that guide the model toward accurate, relevant outputs.
• Retrieval-augmented generation (RAG): Ground responses in up-to-date enterprise data by retrieving relevant documents before generation.
• Guardrails and evaluation: Add output filters and automated quality checks before responses reach end users.

Developers looking to take LLM apps to production need to consider reliable LLM inference at scale alongside governance and monitoring.

How Databricks helps you build reliable LLM-powered agents

Agent Bricks is the unified control plane to build, run, and govern AI agents across any model, provider, or framework. Three pillars address the enterprise gaps described above:

Open and governed

Build with any AI model, OpenAI, Gemini, Llama, Anthropic, and any framework while maintaining enterprise governance. This includes granular access controls, lineage tracking, cost controls, and policy enforcement from AI models down to the underlying data.

Contextual reasoning

Agent Bricks is built natively into the Databricks Platform. It gives agents deep semantic understanding of enterprise data through learned business context, producing state-of-the-art accuracy for document retrieval and processing.

Self-improving

Agent Bricks builds benchmarks using your own data and tasks, then evaluates every output against them. Through prompt optimization, fine-tuning, RLHF, and human feedback, the platform automatically improves performance so agents stay accurate without costly rebuilds.

FAQs

How do large language models work under the hood?

LLMs tokenize a prompt, convert tokens into embeddings, and use a transformer to generate text one token at a time. This inference loop repeats until the output is complete.

What are the key components of a large language model architecture?

The core components are an input embedding layer, stacked transformer blocks with self-attention and feed-forward layers, and an output layer that produces a probability distribution over the vocabulary.

What is the difference between a large language model and traditional NLP models?

Traditional NLP models like RNNs process text sequentially and struggle with long-range dependencies. LLMs use transformer-based parallel processing, far more parameters, and broader training data.

What are common use cases for large language models in business?

Common enterprise use cases include customer support chatbots, content generation, information extraction from documents, code assistance, and multi-agent workflows. Learn more about how real-world enterprises are leveraging generative AI.

How are large language models trained on data?

Models are pre-trained on trillions of tokens to learn general language patterns. Training adjusts billions of parameters using hyperparameters like learning rate, epochs, and batch size.

What are the limitations and risks of using large language models?

Key risks include hallucinations, bias amplification, lack of explainability, and high compute costs. LLMs can also miss critical business context when they lack proprietary data.

What does it mean to fine-tune a large language model?

Fine-tuning further trains a pre-trained model on a smaller, domain-specific dataset. The goal is to specialize a general-purpose model for a particular task or industry.

How do transformers relate to large language models?

LLMs are built on the transformer architecture introduced in the 2017 paper "Attention Is All You Need." Most modern generative LLMs use a decoder-only transformer design.

What are tokens and parameters in the context of an LLM?

A token is a word, subword, or character the model processes. Parameters are the trainable variables, weights and biases, that the model adjusts during training. Most LLMs have at least one billion parameters.

How can developers integrate a large language model into their applications?

Developers integrate LLMs through model-serving endpoints and APIs. On the Databricks Platform, teams can serve any model, govern access through AI Gateway and Unity Catalog, and evaluate outputs with LLM Judges and MLflow.

From LLMs to production-ready AI agents

Large language models are the foundation of modern AI agents. Turning them into reliable enterprise tools requires governance, contextual reasoning, and continuous improvement. Agent Bricks on the Databricks Platform brings these elements together, a unified control plane to build, run, and govern LLM-powered agents across any model or framework. Explore how Databricks supports large language models in production.