Our Take
Basic definitions are useful, but this glossary won't solve the deeper problem that AI terminology shifts faster than publications can track.
Why it matters
Practitioners waste cycles pretending to understand terms like RAG and RLHF in meetings, and clear definitions let teams focus on implementation instead of vocabulary anxiety.
Do this week
Engineering leads: audit your team's AI terminology usage in the next sprint planning to identify knowledge gaps before they cause project delays.
TechCrunch targets AI vocabulary confusion
TechCrunch published a comprehensive AI glossary addressing what they call "an avalanche of new terms and slang" that can "make even very smart people in the tech world feel insecure." The publication frames this as a living document that updates as the field evolves.
The glossary covers 15+ core concepts from AGI (artificial general intelligence) to technical processes like chain-of-thought reasoning and distillation. Each entry provides plain-English explanations with context about why the term matters for practitioners.
Key definitions include practical concepts like API endpoints ("buttons on the back of software that other programs can press"), coding agents (specialized AI that "can write, test, and debug code autonomously"), and hallucination ("the AI industry's preferred term for AI models making stuff up").
Jargon creates real project friction
The terminology problem is acute because AI concepts multiply faster than consensus definitions emerge. Even basic terms like AGI have competing definitions across major labs. OpenAI defines it as systems that "outperform humans at most economically valuable work," while Google DeepMind focuses on "cognitive tasks" specifically.
This definitional chaos hits practitioners directly. Teams spend meeting time clarifying what someone means by "fine-tuning" versus "distillation," or whether "compute" refers to hardware, processing power, or both. The cognitive overhead slows technical decisions.
More importantly, vocabulary gaps mask deeper technical understanding. A developer who knows that "inference" means "running an AI model" can participate in architecture discussions. One who doesn't will either stay silent or make uninformed choices.
Use glossaries as starting points, not endpoints
TechCrunch's approach works for initial orientation but won't solve the core challenge. AI terminology evolves too rapidly for any publication to track completely. New concepts like "memory cache optimization" or "reasoning model architectures" emerge from research labs monthly.
The practical value lies in establishing shared vocabulary within your team. Reference materials like this glossary help normalize asking clarification questions instead of nodding along. That behavioral shift matters more than memorizing definitions.
Focus on the terms that directly affect your implementation decisions. If you're evaluating LLM APIs, understand "fine-tuning," "distillation," and "chain-of-thought reasoning." If you're building agents, prioritize "API endpoints," "memory cache," and "inference" concepts.
The glossary correctly identifies hallucination as arising "from gaps in training data," but practitioners need to know detection and mitigation strategies, not just definitions. Use reference materials to build vocabulary, then dig into technical implementation details that actually affect your systems.