Artificial intelligence is changing software, work and product strategy. It is also forcing readers to learn a new vocabulary that appears in meetings, pitches, podcasts and everyday coverage of the technology.
The terms can sound specialized, but many describe practical ideas: how models are trained, how they respond, why they make mistakes and how developers connect them to real tools. Here is a plain-language guide to the concepts most likely to matter.
The Core Ideas Behind Modern AI
A large language model, or LLM, is the type of AI model behind assistants such as ChatGPT, Claude, Google’s Gemini, Meta's AI Llama, Microsoft Copilot and Mistral’s Le Chat. When a person sends a prompt to one of these assistants, the model processes the request directly or uses available tools such as web browsing or code interpreters.
LLMs are deep neural networks made of billions of numerical parameters, also called weights. They learn relationships between words and phrases from patterns found in billions of books, articles and transcripts. When prompted, they generate the pattern most likely to fit the request.
Deep learning is the broader machine learning approach that makes this possible. It uses multi-layered artificial neural networks to find complex relationships in data. Unlike simpler systems, deep learning models can identify important characteristics in data without human engineers defining each feature in advance.
That capability comes with costs. Deep learning systems need many data points, often millions or more, to perform well. They also typically take longer to train than simpler machine learning algorithms, which can make development more expensive.
How AI Systems Work After Training
Training is what lets a model learn patterns. Inference is what happens when the model is run to make predictions or draw conclusions from data it has previously seen. In simple terms, inference is the active use of a trained AI model.
Many types of hardware can perform inference, including smartphone processors, GPUs and custom-designed AI accelerators. The same model will not run equally well everywhere. Very large models can be slow on a laptop compared with a cloud server using high-end AI chips.
Compute is the shorthand for the computational power that lets AI models train and operate. The term often points to the hardware behind that power, including GPUs, CPUs, TPUs and other infrastructure that supports modern AI systems.
Memory cache is another important efficiency concept. Caching saves certain calculations so a model does not have to repeat them for future queries or operations. KV, or key value, caching works in transformer-based models and can help produce faster results by reducing the work needed to generate answers.
Why Agents And Tools Matter
An AI agent is a tool that uses AI technologies to carry out a series of tasks on a user’s behalf. The idea goes beyond a basic chatbot. An agent may file expenses, book tickets or a restaurant table, or write and maintain code.
The term still means different things to different people because the infrastructure is still developing. At its core, though, it points to an autonomous system that may use multiple AI systems to complete multistep tasks.
Coding agents are a more specific version of this idea. Instead of only suggesting code for a human to paste into a project, a coding agent can write, test and debug code autonomously. It can work across codebases, find bugs, run tests and push fixes with minimal human oversight, though a human still needs to review the result.
API endpoints are part of why agents can become useful. They are interfaces that other programs use to make software perform actions. Developers use them to connect applications, pull data from one service into another, or allow an AI agent to control third-party services without a human manually operating each screen.
Model Context Protocol, or MCP, is an open standard for connecting AI models to outside tools and data, including files, databases and apps like Slack and Google Drive. Anthropic introduced MCP in 2024 and later handed it over to the Linux Foundation. OpenAI, Google and Microsoft have adopted it.
Better Answers, Smaller Models And Persistent Risks
Chain-of-thought reasoning describes a way for large language models to break problems into smaller intermediate steps. It can take longer, but it can improve results, especially for logic or coding tasks. Reasoning models are developed from traditional large language models and optimized for this kind of thinking through reinforcement learning.
Fine-tuning means training an existing AI model further so it performs better in a specific task or area. Many AI startups begin with large language models and add specialized data from a target sector or task to improve usefulness for a commercial product.
Distillation is another model-building technique. Developers send requests to a large teacher model, record its outputs and use those outputs to train a smaller student model. The goal can be a smaller, more efficient model that behaves similarly to the larger one with minimal distillation loss.
Mixture of Experts, or MoE, is an architecture that splits a neural network into smaller specialized subnetworks called experts. For a given task, only some experts are activated. A router chooses which specialists to use, making it possible to build very large models that remain relatively fast and cheap to run.
Hallucination is the industry term for AI models generating incorrect information. It is a major quality problem because generated answers can mislead users and, in some cases, create real-life risk, such as harmful medical advice in response to a health query.
The source describes hallucinations as a likely consequence of gaps in training data. That risk is one reason for interest in specialized or vertical AI models that focus on narrower domains and may reduce knowledge gaps and disinformation risks.
Open Questions Around Capability And Access
Artificial general intelligence, or AGI, remains a fuzzy term. It generally refers to AI that is more capable than the average human at many, if not most, tasks. OpenAI CEO Sam Altman once described AGI as the “equivalent of a median human that you could hire as a co-worker.”
OpenAI’s charter defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.” Google DeepMind describes it as “AI that's at least as capable as humans at most cognitive tasks.” Even experts at the forefront of AI research do not agree on a single definition.
Open source is another major debate. In software and AI models, it means the underlying code is publicly available for others to use, inspect or modify. Meta's Llama family is a prominent AI example, while Linux is the historical operating-system parallel.
Closed source systems keep the code private. Users can access the product but cannot inspect how it works, as with OpenAI's GPT models. The difference now sits at the center of arguments over progress, competition and safety audits in the AI industry.