Large language model
A large language model is a neural network trained on massive text datasets to understand and generate human-like language, enabling it to perform diverse tasks from content creation to complex reasoning without task-specific programming.
How large language models work
Large language models operate through a transformer architecture that processes text as sequences of tokens. These models use self-attention mechanisms to understand relationships between words and capture the complexities of language, enabling them to generate contextual responses by analyzing meaning and context.
During training, LLMs learn statistical patterns from billions of words through self-supervised learning—typically by predicting the next word in a sequence. This foundational training allows them to develop capabilities like in-context learning and multi-step reasoning that weren't explicitly programmed. The result is a model that can understand nuanced language and tackle diverse tasks without being specifically trained for each one.
Core architecture and training
The transformer architecture serves as the backbone for most modern large language models. There are three main variants: encoder-only models like BERT for language understanding, decoder-only models like GPT for text generation, and encoder-decoder models like T5 for sequence-to-sequence tasks.
Training happens in multiple phases. First comes pre-training on massive unlabeled text datasets, followed by fine-tuning using labeled data or instruction tuning with techniques like Reinforcement Learning from Human Feedback (RLHF). The scale is substantial—these models contain tens to hundreds of billions of parameters and train on datasets spanning hundreds of gigabytes to multiple terabytes of text.
Enterprise applications in practice
Beyond customer-facing applications, LLMs excel at document analysis, language translation, and code generation. Google reports that 25% of new code is now generated by AI, with models like Gemini assisting in test generation and code review. They help with market research by extracting insights from text data, assist in cybersecurity threat detection, and automate routine content workflows. The key is finding the right balance between automation and human oversight to ensure quality and accuracy.
However, these models have significant limitations. They can hallucinate by generating plausible but incorrect information, lack persistent memory and real-time knowledge, and require substantial computational resources. They may also inherit biases from training data. When working with real business data, accuracy becomes a critical concern—studies show LLM products achieving only 22% accuracy with insurance company data, dropping to zero for complex, expert-level requests. Furthermore, hallucination rates for legal queries can be as high as 69-88%, with models like GPT-4o and Claude 2 exhibiting 40-50% hallucination rates in open domains.
However, these models have significant limitations. They can hallucinate by generating plausible but incorrect information, lack persistent memory and real-time knowledge, and require substantial computational resources. They may also inherit biases from training data. When working with real business data, accuracy becomes a critical concern—studies show LLM products achieving only 22% accuracy with insurance company data, dropping to zero for complex, expert-level requests.
Capabilities and limitations
Large language models demonstrate impressive zero-shot and few-shot learning abilities—they can perform tasks without specific training examples. They excel at in-context learning, instruction following, and multi-step reasoning through techniques like chain-of-thought prompting.
However, these models have significant limitations. They can hallucinate by generating plausible but incorrect information, lack persistent memory and real-time knowledge, and require substantial computational resources. They may also inherit biases from training data. When working with real business data, accuracy becomes a critical concern—studies show LLM products achieving only 22% accuracy with insurance company data, dropping to zero for complex, expert-level requests.
Glean's approach to large language models
At Glean, we understand that large language models alone aren't sufficient for enterprise success. They need the right foundation to deliver reliable, contextual results that businesses can trust.
Rather than building our own LLMs, we focus on what matters most: optimizing the retrieval system that feeds these models with accurate, permissions-aware company knowledge. Our approach combines LLMs with advanced search technology and knowledge graphs that map all the content, people, and activity within an enterprise.
Through Retrieval Augmented Generation (RAG), we ground LLM responses in verified company knowledge, preventing hallucinations and ensuring AI assistants provide trustworthy answers while respecting data permissions. We believe the future lies in agentic reasoning systems that can plan, execute, and adapt—transforming LLMs from simple question-answering tools into intelligent work partners that understand your organization's unique context and help employees accomplish complex workflows.
Frequently asked questions about large language models
What makes a language model "large" compared to traditional ai models?
The "large" designation refers to both the massive number of parameters (billions to trillions) and the enormous scale of training data (terabytes to petabytes of text). This scale enables emergent capabilities and sophisticated language understanding that smaller models cannot achieve.
How do large language models learn without being explicitly programmed for specific tasks?
Large language models use self-supervised learning, typically by predicting the next word in a sequence across billions of text examples. This process allows them to internalize language patterns, grammar, and knowledge without task-specific programming, enabling them to perform well on new tasks they've never seen before.
What are the main security and privacy concerns with LLM deployment in enterprises?
Privacy and data security top the list of enterprise concerns. Companies are hesitant to share sensitive financial, medical, and personal information with large language models. Additional concerns include data leakage, model hallucinations, and ensuring proper access controls that align with existing permissions structures.
Can large language models replace human workers in enterprise environments?
Large language models work best as productivity multipliers rather than replacements. They excel at automating routine tasks and handling repetitive workloads, freeing employees to focus on strategic work that requires creativity, critical thinking, and human judgment. The goal is augmentation, not replacement.
How do large language models handle different languages and cultural contexts?
LLMs can break down language barriers through real-time translation and localization services, often understanding cultural nuances to provide contextually appropriate content. However, performance varies significantly across languages, with models typically performing best in languages that were well-represented in their training data.
