LLMs are the backbone of AI tools that process and generate natural language

LLMs are trained via modeling, imitation learning, and reinforcement learning

Training large language models begins with pretraining the model to predict the next word in a sequence based on finding patterns in massive amounts of text. Patterns are then fine-tuned to model human-written dialogue and to align responses with user preferences.

LLMs explained simply and quickly

Large language models generate responses by predicting one token at a time, based on patterns they’ve learned from massive datasets. This explainer covers their core components: data, architecture, training, and how transformer models use context to simulate conversation.

How ChatGPT learns to read

The introduction of ChatGPT last fall jumpstarted public interest in the future capabilities of artificial intelligence and what it means for society. Of many somewhat "jargony" terms thrown around, the phrase "Large Language Model" is commonly seen—but also incredibly unclear as to what it means. In the simplest sense, the programs comb through mountains of internet data in response to a question and guess at what should come next (subscription needed).

Transformer architecture can recognize and predict patterns in language

The underlying software powering text generation in AI tools associates each word or subword—called a token—with a set of values corresponding to how often it appears near other tokens. By recognizing these associations in prompts, the LLM can infer meaning.

Visualize how transformer architecture generates an LLM's outputs

Transformer architecture is the neural network framework upon which large language models predict and generate text based on user inputs. This interactive lets you explore the "thinking" a GPT does to generate responses based on your input.

Generative AI tools excel at pattern recognition, not contextual accuracy

Large language models are trained on vast amounts of unstructured data, from which they develop parameters for grammar and associations between words. These connections can introduce errors due to inapplicable reasoning when used on new data in unfamiliar contexts.

View a timeline of AI deployments from 2017 to 2025

The development of transformer architecture and the release of GPTs across AI labs led to rapid changes in LLM scale and power. Companies like OpenAI, Google, Meta, Microsoft, and Anthropic have invested billions of dollars to develop reliable, versatile models.

Anthropic's interpretability team discusses how AI models' thinking mimics biology

Just as humans engage in complex behaviors to survive and reproduce, teams of experts in neuroscience, virology, mathematics, and other disciplines argue that large language models develop complex mechanisms to achieve their goals. These mechanisms can be manipulated to assess how they modify outputs—much like stimulating individual neurons—to uncover how LLMs "think."

Tokenization breaks text into pieces that LLMs use to process language

Instead of understanding the meaning of words, large language models break up sentences into words and subwords, assigning each a set of numbers called tokens. Grammar rules are then "learned" by finding associations between these sets of numbers.

Large language models are examples of foundation models

Such models are trained on massive amounts of data, which enable them to perform a variety of tasks, rather than being provided task-specific data to complete a narrow function. Such models come with high compute costs and potential trust issues due to the nature of their unorganized training data.

Explore rankings of large language models

The SEAL LLM Leaderboards provide benchmarks for LLMs by assessing them against agentic, frontier, and safety tasks, providing insights into each model's strengths, deficiencies, and failures.

In 2023, OpenAI spent $520 million on ChatGPT

The ongoing development and refinement of new AI models requires larger datasets and processing more parameters between words and subwords to improve the accuracy of generated content. New startups are expected to harness this technology to provide unthought-of products and services.

Large language models struggle to play video games, despite coding them easily

As of 2025, given clear tasks, specific feedback, and test results to learn from, LLMs have been successful at coding simple video games that resemble pre-existing ones. However, given the variety of games, the lack of training datasets, and the need for spatial awareness, which LLMs find challenging, some of these models experience greater difficulty navigating and "winning" games.

Understanding how Claude Code and other AI coding agents function

Under human oversight, a supervising large language model interprets user tasks and delegates work to subordinate LLMs, which can generate code, fix bugs, and run tests, often most effectively for proofs of concept. Incremental backups and versioning are crucial when using such agents, which can lose details during their work as a result of compressing context history to work around memory limitations.

Neural networks are modeled after an idealization of the brain

These networks—including the backbone of ChatGPT-3, which has approximately 175 billion parameters—naturally discover the implicit rules of grammar and syntax within language by identifying patterns. Deviations in word patterns add creativity to the text generation.

Model context protocol allows large language models to connect to external data

Using a unified interface, MCP simplifies how AI systems securely connect to external data, tools, and services. The protocol enables AI agents to perform tasks without ongoing human refinement or updates to manually integrate new data.

Machine learning lets computers learn from data instead of relying on programming

ML is a core part of artificial intelligence and is foundational to applications like chatbots, fraud detection, and self-driving cars. Businesses are also adopting it to improve efficiency and identify patterns in data, though the latter may be inaccurate.

AI scraping for LLM training data has significantly strained Wikipedia's infrastructure

From January 2024 to April 2025, the site's bandwidth increased by 50% as automated bots downloaded terabytes of data for the large language models powering AI tools. The Wikimedia Foundation found that bots accounted for 65% of the highest demand requests (e.g., videos) despite representing just 35% of page views.

Trick an LLM into revealing sensitive information

Even with robust training, large language models may be fooled into providing sensitive information if correctly prompted. In this game, a wizard withholds eight passwords, which you must prompt them to reveal with ever-increasing LLM safeguards.

Studies show AI tools can result in passive learning with less retention

Although ChatGPT, Google's AI overviews, and other similar software can save time, a study of more than 10,000 adults showed that reliance on them yielded work products that were more generic and included fewer facts than those produced solely by Google search. Weaker brain connectivity has also been observed when users write using AI.

Temperature is a value that controls how predictable or creative an LLM's output is

In large language models, temperature adjusts the randomness in text generation by modifying how often words with lower probabilities are selected next in the sequence. Lower temperatures produce more obvious outputs while higher temperatures produce more creative responses.

Artificial general intelligence equals or surpasses human intelligence

AGI incorporates sensory perception, memory, and advanced logical inference systems to move beyond the narrow tasks seen in large language models and chatbots. One of the most significant hurdles in developing AGI is designing systems that learn and can flexibly apply learning across domains.

There’s an artificial intelligence model trained on bankruptcy law

Although it's designed for lawyers, the tool is able to help everyday browsers synthesize important cases and point users to relevant documents about them. One sample using it asks Chatlaw to explain the significance of a recent case and then to define relevant terminology.

Explore persistent 3D environments with Marble

Marble is a spatial AI system built by World Labs, a San Francisco startup focusing on world models (rather than large language models). Using a text prompt (or a single image or video), users can generate a "world" and move through it, edit it, or export it. The company and project are part of the broader spatial intelligence area of AI, where AI can simulate environments.

The hardware powering AI was originally designed for video game graphics processing

Graphics processing units are specialized computer chips that can process multiple data streams in parallel, including pixel colors for a display. Such processing is required to find connections across text data and generate outputs quickly.

English mathematician Ada Lovelace wrote the first computer algorithm a century before computers existed and hypothesized that computing devices would one day be able to compose music—foreseeing the generative AI systems of today.

Watch how algorithms can be designed to train themselves and self-improve without human input, to the point where the optimized code is too complex for programmers to understand.

This site reaches millions of visitors who are tired of AI chatbots and seeking human connection. Pretend to be an AI chatbot and ask questions, provide answers, and draw for people in real time.

In 2025, four executives from large tech companies were given positions in the US Army Reserves—including Palantir Chief Technology Officer Shyam Sankar, which reinforced Palantir's ties with the US military and its role as a leading defense partner.

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