TechCrunch Releases AI Glossary to Demystify Industry Jargon

Artificial intelligence is rewriting how we work, but it's also rewriting the language we use to describe it. Spend five minutes reading about AI and you'll run into LLMs, RAG, RLHF, and a dozen other acronyms that can make even seasoned tech professionals feel insecure. TechCrunch addressed this confusion with a new glossary published on May 9, 2026, designed to clarify the terminology flooding tech reporting.

The publication frames the guide as a living document, much like the AI systems it describes. That's fitting, given how rapidly definitions shift in this space. The glossary covers core concepts including large language models, hallucinations, AI agents, chain-of-thought reasoning, and the infrastructure powering it all.

At the foundation sits the large language model, or LLM. These are the AI models behind assistants like ChatGPT, Claude, and Google Gemini. When users interact with an AI assistant, they're interacting with an LLM either directly or through a tool. LLMs are deep neural networks composed of billions of numerical parameters that learn relationships between words and phrases. They're built by encoding patterns from vast amounts of books, articles, and conversation logs. When prompted, an LLM generates the pattern most likely to match, predicting subsequent content word by word.

Then there's the elephant in the room: hallucination. This is the industry term for when an AI model generates incorrect information, essentially making things up. The problem stems from gaps in training data or the model's over-generalization of patterns. It can lead to misleading outputs and real-world risks, such as harmful advice in medical consultations. Most generative AI tools include reminders in their terms of use for users to verify answers. The guide emphasizes that users need to remain vigilant (a problem that has plagued users for years, frankly).

AI agents represent another critical concept. An AI agent refers to a tool that uses AI technologies to perform a series of tasks on your behalf — beyond what a more basic AI chatbot could do. Think filing expenses, booking tickets or a table at a restaurant, or even writing and maintaining code. The basic concept implies an autonomous system that may draw on multiple AI systems to carry out multistep tasks. Infrastructure is still being built out to deliver on its envisaged capabilities, so "AI agent" might mean different things to different people.

Coding agents are a more specific version of this concept. Rather than simply suggesting code for a human to review and paste in, a coding agent can write, test, and debug code autonomously. These agents can operate across entire codebases, spotting bugs, running tests, and pushing fixes with minimal human oversight. Think of it like hiring a very fast intern who never sleeps and never loses focus — though, as with any intern, a human still needs to review the work.

Chain-of-thought reasoning is another key technique. Given a simple question, a human brain can answer without thinking too much about it. But in many cases, you need a pen and paper to come up with the right answer because there are intermediary steps. In an AI context, chain-of-thought reasoning for large language models means breaking down a problem into smaller, intermediate steps to improve the quality of the end result. It usually takes longer to get an answer, but the answer is more likely to be correct, especially in a logic or coding context.

API endpoints deserve mention too. Think of API endpoints as "buttons" on the back of a piece of software that other programs can press to make it do things. Developers use these interfaces to build integrations — for example, allowing one application to pull data from another, or enabling an AI agent to control third-party services directly without a human manually operating each interface. As AI agents grow more capable, they are increasingly able to find and use these endpoints on their own, opening up powerful — and sometimes unexpected — possibilities for automation.

Compute is the fuel behind all of this. Although somewhat of a multivalent term, compute generally refers to the vital computational power that allows AI models to operate. The term is often a shorthand for the kinds of hardware that provides the computational power — things like GPUs, CPUs, TPUs, and other forms of infrastructure that form the bedrock of the modern AI industry. You can't train or run these models without it.

Deep learning is a subset of self-improving machine learning in which AI algorithms are designed with a multi-layered, artificial neural network structure. This allows them to make more complex correlations compared to simpler machine learning-based systems. The structure of deep learning algorithms draws inspiration from the interconnected pathways of neurons in the human brain. Deep learning AI models are able to identify important characteristics in data themselves, rather than requiring human engineers to define these features.

AGI, or artificial general intelligence, remains the most nebulous term. It generally refers to AI that's 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." Meanwhile, OpenAI's charter defines AGI as "highly autonomous systems that outperform humans at most economically valuable work." Google DeepMind's understanding differs slightly; the lab views AGI as "AI that's at least as capable as humans at most cognitive tasks." Confused? Not to worry — so are experts at the forefront of AI research.

Training and inference represent two distinct phases in AI development. Training is the process of feeding data into an AI model to make it learn from patterns and generate useful outputs. This requires a massive amount of input and is costly. Inference, on the other hand, is the process of running a trained model to make predictions, complementing training. A neural network is a multi-layered algorithmic structure inspired by the connections of neurons in the human brain, supporting deep learning to achieve complex associations.

The guide arranges entries in alphabetical order and provides cross-references to be clear and easy to understand. TechCrunch states it will regularly add new entries to keep up with the methods researchers use to push the frontiers of AI and emerging security risks. Readers can use this resource to quickly grasp key vocabulary in AI reporting and enhance their understanding of the industry.

Independent reporting from Houdao corroborates the scope and timing of the publication, noting the guide's focus on helping readers better understand terminology in tech reporting.

The original glossary is available at TechCrunch's official site, where it will continue to evolve alongside the field itself.

Whether this glossary actually helps or just adds another layer of jargon to memorize remains to be seen. The real test is whether developers, journalists, and business leaders can stop nodding along and start asking better questions. That's the only metric that matters.

Artūras Malašauskas is an AI Systems Integrator with 20+ years of production-grade web engineering experience. He has designed, shipped, and scaled enterprise Python/PHP systems for logistics, SaaS, and public-sector clients. For the past year, he has focused exclusively on AI integrations: deploying open-source LLMs, building generative media pipelines (image, audio, video), and engineering multi-agent workflows for real production environments. His standard: reproducibility, security, cost-efficient inference—no vaporware. He documents and evaluates emerging AI tooling, separating verified capabilities from marketing noise. Technical editor at: muza-ai.eu, ai-verslas.lt, ai-naujinos.lt Connect on LinkedIn