The only AI glossary you'll need this year, explained

TechCrunch has published the only glossary you'll need this year — a living AI reference that translates jargon like LLMs, RAG, and RLHF into plain English. Updated July 3, 2026, it defines terms builders, investors, and curious readers actually encounter, from hallucinations and AI agents to compute shortages dubbed RAMageddon.

Artificial intelligence is rewriting the world and inventing a new language to describe how. Sit in on any product meeting, pitch, or panel and you will hear acronyms that can make even smart tech insiders feel insecure. TechCrunch's guide is built to fix that gap with plain-language definitions of the AI terms you are most likely to run into.

Key Takeaways

• TechCrunch calls its July 2026 update the only AI glossary you'll need this year, and treats it as a living document updated as the field evolves.
• Core concepts include LLMs, AI agents, chain-of-thought reasoning, Model Context Protocol (MCP), and Mixture of Experts (MoE) architectures.
• Hallucinations — when models generate incorrect information — are pushing the industry toward narrower, domain-specific AI systems.
• Hardware and economics terms such as compute, inference, token throughput, and RAMageddon explain why AI demand is straining memory supplies and consumer tech prices.
• For ongoing coverage, browse our Future Tech & AI Wonders section.

Why does AI jargon matter right now?

Generative AI has spread faster than a shared vocabulary. Builders reference API endpoints and coding agents; investors ask about distillation and fine-tuning; everyday users wonder why chatbots sometimes fabricate answers. Without a common dictionary, conversations about safety, cost, and capability break down quickly.

TechCrunch positions its glossary for anyone building with AI, investing in it, or simply trying to follow the news. The July 3, 2026 refresh keeps pace with a field that renames concepts almost as fast as it ships products.

What is hallucination in AI?

Hallucination is the AI industry's preferred term for models making things up — literally generating information that is incorrect. TechCrunch notes it is a huge problem for AI quality because misleading GenAI outputs can carry real-life risks, such as harmful medical advice from a health query.

The guide links hallucinations to gaps in training data. That limitation is contributing to a push toward increasingly specialized vertical AI models designed to shrink knowledge gaps and reduce disinformation risks. Understanding the term helps explain why general-purpose chatbots and narrow enterprise tools are diverging.

Which AI terms should you learn first?

Large language models (LLMs) power popular assistants like ChatGPT, Claude, Gemini, and Copilot. They are deep neural networks with billions of parameters that learn relationships between words from vast text corpora. When you prompt an LLM, it generates the most likely pattern that fits your request.

Chain-of-thought reasoning breaks problems into intermediate steps to improve accuracy, especially in logic or coding. Reinforcement learning from human feedback (RLHF) nudges models toward preferred behaviors. Model Context Protocol (MCP), introduced by Anthropic in 2024 and later handed to the Linux Foundation, acts like a USB-C port for AI — letting models connect to external tools without custom connectors for every pairing.

On the infrastructure side, compute refers to the processing power behind training and deployment. Inference is running a trained model to make predictions. Token throughput measures how much language a system processes at once — a key factor in how many users a model can serve simultaneously.

How is AI hardware reshaping the wider tech market?

TechCrunch's glossary also captures economic ripple effects. RAMageddon describes a growing shortage of RAM chips as AI labs buy massive memory volumes for data centers, leaving less supply for gaming consoles, smartphones, and enterprise computing. Prices are climbing across consumer electronics as a result.

Parallelization — performing many calculations at once — is why GPUs became the backbone of modern AI. Memory caching, including KV caching in transformer models, cuts redundant calculations during inference to save power and speed responses. These terms connect abstract model benchmarks to the hardware constraints shaping product roadmaps.

The full reference spans additional entries on AGI, diffusion, GANs, open source debates, recursive self-improvement, and validation loss. TechCrunch notes the article is updated regularly with new information. Read the complete TechCrunch AI glossary for every definition.