If you’ve been nodding through AI conversations without actually understanding them, this kind of glossary is overdue. The latest TechCrunch explainer is less a news story than a translation layer for the language now dominating product decks, vendor demos, and developer tooling.
What makes this moment interesting is not that AI suddenly became real, but that the vocabulary around it finally escaped research labs and landed in everyday software work. Terms like LLMs, RAG, multimodal, agentic AI, and hallucinations are no longer niche jargon; they are now part of how companies sell features and how developers evaluate them.
<> The most useful AI glossaries do one thing well: they separate capability from marketing./>
That distinction matters because the industry loves to blur it. AI is the umbrella term; machine learning is a subset; generative AI is the branch that creates new text, images, code, audio, or video rather than merely classifying data. Those distinctions sound academic until you realize they determine what a system can actually do, how it fails, and whether the claims around it are even remotely credible.
The current wave of AI vocabulary also reflects a deeper shift in software design. Prompt engineering matters because models are extremely sensitive to instructions. Context windows matter because models can only consider so much text at once, which shapes everything from chat apps to document analysis. RAG matters because it gives a model outside knowledge to ground its answers, which is one of the few practical ways to reduce hallucinations without pretending the model “knows” things it doesn’t.
That last point is where the industry still does itself no favors. Hallucination is a blunt word for a blunt problem: fluent output that is false, unsupported, or fabricated. It sounds almost whimsical, which is part of the problem. In production systems, hallucinations are not cute; they are defects.
A good glossary should also puncture the fantasy that today’s systems are human-like. Government and academic glossaries consistently frame current AI as narrow or task-specific, while AGI remains hypothetical rather than a deployed reality. That is an important corrective to the way some vendors talk. “Agentic AI” may sound like the beginning of digital autonomy, but in practice it is often just workflow automation with better branding.
For developers, the practical takeaway is simple:
- Learn the terms, but don’t trust the vibes.
- Measure the workflow, not the slogan.
- Test for failure modes like bias, randomness, and hallucination, not just demo-day polish.
The real value of this kind of explainer is that it lowers the language barrier without pretending the technology is simpler than it is. AI is becoming a standard part of software, but the people building with it still need a clear mental model of what these systems are, what they aren’t, and where the marketing machine is doing most of the work.
That’s why glossaries matter now: not because the field lacks jargon, but because it has far too much of it.
