As a founder building AI-native products, it irks me how often startups (and even large enterprises) misuse AI buzzwords. A chatbot bolted onto a product does not make it “AI-First.” So, if you’re a business leader trying to cut through the noise, here’s a no-nonsense glossary that will help you separate real innovation from marketing fluff. I’ve also included practical business use cases to make it real.
1. Foundational Model
What it is: Massive pre-trained models (like GPT, Claude, Gemini) that serve as the “brain” behind AI products.
Business Example: GPT-4 can power customer service automation, legal summarisers, or internal research tools — but only after tailoring it to your business context.
2. Large Language Model (LLM)
What it is: A specialised foundational model focused on understanding and generating human language.
Business Example: Used to auto-draft emails, summarise reports, or power smart search on your knowledge base.
3. AI Assistant
What it is: A chatbot-like interface built on top of an LLM, enriched with your data and business logic.
Business Example: An internal assistant for employees to query HR policies, product documentation, or workflows.
4. AI Agent
What it is: An autonomous or semi-autonomous AI system that performs tasks and makes decisions across tools and systems.
Business Example: An AI agent that handles onboarding by generating documents, sending emails, and updating CRM entries.
5. RAG (Retrieval-Augmented Generation)
What it is: A method that lets LLMs retrieve relevant documents in real-time instead of relying on static training data.
Business Example: Your sales team can ask, “What are our current enterprise pricing tiers?” and get answers from the most up-to-date internal documents.
6. Embeddings
What it is: High-dimensional vector representations of text, used to find semantically similar documents.
Business Example: Powering search across thousands of support tickets to find related cases and responses.
7. Fine-Tuning
What it is: Training a foundational model further on your proprietary data.
Business Example: A financial services firm fine-tunes a model on its regulatory data to ensure compliance-aware AI outputs.
8. Zero-Shot & Few-Shot Prompting
What it is: Techniques to guide LLM behavior using examples (few-shot) or clear instructions (zero-shot).
Business Example: Giving the model one or two examples of tone and format so it consistently drafts blog posts in your brand voice.
9. Vector Database
What it is: A database built to store embeddings and perform fast similarity searches.
Business Example: Back-end infrastructure that makes an AI-powered knowledge base instant and accurate.
10. Hallucination
What it is: The AI confidently giving false or made-up answers.
Business Tip: Only trust vendors who can show safeguards like RAG pipelines, source citations, and fallback logic.
11. Multi-Agent Systems
What it is: Orchestrating multiple AI agents that coordinate with each other to complete complex tasks.
Business Example: A product development workflow where one agent drafts specs, another validates feasibility, and a third schedules tasks.
12. Guardrails
What it is: Safety and control frameworks for AI outputs.
Business Example: Preventing legal or compliance-violating responses in customer support.
Contrarian Terms You Should Be Wary Of:
- “AI-First”: This means your product wouldn’t exist without AI as the core logic — not just a chatbot bolted on.
- “Copilot”: Often just a marketing term for glorified autocomplete.
- “Autonomous AI”: Ask what fallback mechanisms exist; most systems aren’t truly autonomous.
Final Thought
Don’t let marketing jargon cloud what AI can really do for your business. Whether it’s automating internal workflows, transforming customer support, or turning your documentation into a competitive advantage — it’s all possible, but only with clear understanding and the right architecture.
➡️ Book a free consultation call with us to explore how AI can transform your business — and cut through the noise.