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20+ Commonly Used Gen AI Terms to Know This Year!

  • Post Date
    July 31, 2025

 

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Generative AI has progressed from early proof-of-concept technology to a strategic growth engine reshaping how organisations create products, personalise customer journeys, streamline workflows, and unlock new revenue streams. Board-level discussions no longer ask whether to adopt Gen AI, but rather which foundation model to fine-tune, which data pipelines to build, and how quickly prototypes can reach production.

Yet these conversations often descend into alphabet soup. Acronyms like LLM, RAG, and NLP appear alongside terms such as “vector database” and “prompt engineering,” with little explanation. When executives cannot decode this jargon, assessing opportunities or mitigating risks, becomes a guessing game. Conversely, teams that share a clear Gen AI vocabulary move faster from ideation to proof of concept to deployment, turning technology into measurable ROI.

This glossary closes that gap. It offers concise, business-focused definitions and explains why each concept matters, enabling you to brief stakeholders, challenge vendors, and plan with confidence.

💾 Download Your Free 1-Page “20+ Gen AI Glossary” Here!

Core Concepts of Generative AI

To engage meaningfully in any AI initiative, one must first be fluent in the discipline’s foundational vocabulary. The six terms in the following section establish the conceptual framework on which all subsequent discussion depends.

Generative AI

Models that create new text, images, audio, video, or code by learning patterns from massive datasets. Why it matters: Enables rapid content production, personalised engagement, and entirely new digital products.

Machine Learning

Algorithms that learn statistical patterns from data and improve over time. Why it matters: They power demand forecasting, fraud detection, recommendation engines, and more.

Large Language Model (LLM)

Huge neural networks—often hundreds of billions of parameters—trained to predict the next token in a text sequence. Why it matters: Drives human-like chatbots, search summarisation, and large-scale document analysis.

Natural Language Processing (NLP)

The field that enables machines to understand, interpret, and generate human language. Why it matters: Underpins sentiment analysis, multilingual support, and voice assistants.

Model

The combination of architecture and learned weights that transforms inputs into outputs. Why it matters: Model choice shapes accuracy, compliance, cost, and latency.

Dataset

Curated text, images, or other data used to train or fine-tune a model. Why it matters: Data quality directly affects performance, bias, and regulatory risk.

Communicating with the Model

With the fundamentals in place, the next focus is on effective interaction. The terms that follow clarify how prompts are constructed and how textual units are managed to direct model behaviour with precision.

Prompt

The instruction or question you feed an AI model. Why it matters: Clear, well-scoped prompts produce more accurate, useful outputs.

Token

The smallest text unit—often a word fragment—into which inputs are split. Why it matters: Token limits define the model’s context window and influence cost and latency.

Zero-Shot Learning

A model’s ability to solve tasks it was never explicitly trained on. Why it matters: Accelerates experimentation and reduces the need for labelled data.

Under the Hood — How Models Learn & Reason

Interaction alone is insufficient for robust AI adoption. Leaders and architects must also understand the processes by which models are trained, specialised, and executed. This section summarises the model-development lifecycle and the vector-based representations that underpin modern semantic search.

Training

The compute-intensive process of adjusting model weights across billions of examples. Why it matters: Establishes baseline capability and typically requires cloud-scale resources.

Fine-Tuning

Additional training on domain-specific data to specialise a pre-trained model. Why it matters: Aligns outputs with proprietary knowledge, creating competitive advantage.

Inference

The stage where a trained model processes new inputs and produces outputs. Why it matters: Latency and per-call cost directly impact user experience and margins.

Embedding

A high-dimensional vector that captures the semantic meaning of data. Why it matters: Enables semantic search, similarity matching, and personalised recommendations.

Vector Database

A specialised store optimised for indexing and querying embeddings. Why it matters: Retrieves relevant context in milliseconds—crucial for grounded chatbots and search applications.

Operational & Architectural Components

Having examined model development, we now address the architectural elements that operationalise AI capabilities. The terms below cover the interfaces and infrastructure required to embed language models within enterprise systems and deliver measurable value.

API

Network endpoints that expose model capabilities to other software. Why it matters: Lets developers embed LLM power into CRMs, ERPs, and custom apps without managing infrastructure.

Real-Time AI

Systems designed for sub-second inference latency. Why it matters: Instant responses are essential for live chat, voice transcription, and fraud monitoring.

Agent

A goal-oriented system that combines planning, memory, and tool use to execute multi-step tasks autonomously. Why it matters: Automates complex workflows end-to-end, boosting productivity.

Chatbot

A text- or voice-based conversational interface powered by an AI model. Why it matters: Reduces support costs while providing 24/7 service.

Next-Wave Concepts & Key Considerations

The AI landscape is advancing rapidly. The final section highlights emerging concepts and strategic considerations that merit close attention as organisations scale from proof-of-concept to enterprise-wide deployment.

Hallucination

Generative models may produce fluent but factually incorrect or fabricated content. Mitigation: retrieval-augmented generation (RAG), human-in-the-loop review, and rigorous validation.

Multimodal Model

Architectures trained on—and able to generate—multiple data types (text, image, audio). Unlocks applications like describing images, generating slide decks from voice, or producing videos from scripts.

RAG

Combines vector search (retrieval) with a generative model conditioned on the retrieved documents, lowering hallucination rates and keeping answers up-to-date without full model retraining.

Open Source Model

A model whose architecture and weights are publicly released under a permissive licence. Offers transparency and lower total cost of ownership but shifts MLOps responsibility in-house.

Artificial General Intelligence (AGI)

The aspirational next stage of AI: a system that can grasp concepts, learn from limited data, and apply knowledge across any domain with human-level (or greater) depth and flexibility.

Final Thought

Fluency in AI terminology is a strategic asset. Leaders who understand concepts like vector databases or RAG pipelines make faster, better-informed decisions about build-versus-buy, risk management, and long-term capability investment. Keep this glossary handy, share it with your teams, and revisit it often—the language of Generative AI will evolve as rapidly as the technology itself.

Need help turning these terms into real-world ROI? Our experts can map high-impact use-cases and launch a proof of concept in just a few days!

See how Gen AI can transform your business!
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