Let's make AI a little less complicated. Here’s a straightforward guide to the terms that matter when you’re using AI — whether to analyze data, communicate with customers, or automate daily work.

Artificial Intelligence (AI)

Software that performs tasks that normally require human intelligence — spotting patterns, predicting outcomes, or generating language and visuals. You’ll see AI in everything from customer-service chatbots to marketing tools to secure analytics platforms like Alkemi — anywhere systems are built to think.

Large Language Model (LLM)

An AI system trained on massive collections of written text so it can understand, predict, and generate human language — the way we naturally communicate through words, tone, and context.

“Human language” includes everything from short Slack messages to detailed policy docs — nuanced, idiomatic, and sometimes ambiguous. In contrast, computer language is literal and rule-based — code, SQL queries, or formulas that require exact syntax.

LLMs bridge that gap, allowing humans to talk to technology in plain English. Examples include ChatGPT, Gemini, and Claude — great for text-based tasks like summarizing reports, writing drafts, or answering questions. However, because they’re trained on open data, they aren’t designed to safely analyze your company’s private or regulated information.

Generative AI

AI that creates something new — text, images, code, or insights — instead of just analyzing existing information. It’s used in tools that write marketing copy, design graphics, compose emails, or explain data trends.

Alkemi uses this kind of AI to describe what’s happening in your business data — not to invent or speculate.

Inference

Inference is the moment when an AI system applies what it knows to generate a new answer or output from your input.

Every time you ask a model a question (“What’s next week’s forecast?” or “Summarize this report”), it’s performing an inference — using trained patterns to make a reasoned prediction or response. It’s essentially the “thinking step” between your question and the AI’s answer.

At Alkemi, every inference runs securely inside your company’s environment — meaning the model processes data privately, without sending it to external servers or training on your information.

Inference Security

Protecting your data while AI “thinks.” Inference security means an AI system can process and respond to your data without copying, storing, or training on it. That principle matters anywhere AI touches proprietary or customer information — from internal analytics to customer-support chatbots.

Data Governance (Governed Data)

Data governance — sometimes called governed data — simply means knowing where your data comes from, who can use it, and how it’s protected.

Think of it like digital hygiene: organized, labeled, and verified information your business can trust. Governed data is the opposite of messy folders and mystery spreadsheets. It ensures AI systems make decisions based on accurate, approved information — not outdated files or personal uploads.

When data is governed, everyone — from finance to marketing, analyst or not-so-technical — can explore it confidently, knowing they’re using the same, reliable source of truth.

Hallucination

When AI confidently generates something that isn’t true.

For example, it might:

  • Make up a source or statistic that doesn’t exist
  • Invent a “quote” from a public figure
  • Misstate a business metric or trend

Hallucinations happen when AI fills gaps in its knowledge with guesses that sound right.
They’re risky in business because even one false claim — in a report, customer email, or investor deck — can mislead decisions or damage credibility.

Alkemi prevents hallucinations by grounding every answer in verified company data, so the model can’t improvise or fabricate.

Retrieval-Augmented Generation (RAG)

A technique used by many AI assistants to reduce hallucinations and improve accuracy. The model first retrieves relevant, factual information from trusted sources, then uses it to generate an answer — like open-book AI.

RAG is the foundation for many enterprise-grade systems, including Alkemi, that prioritize grounded, explainable outputs.

Conversational AI

AI that understands and responds to natural language — so people can ask questions or give instructions in everyday words. It powers everything from customer-service chatbots (“Where’s my order?”) and HR assistants (“How many vacation days do I have left?”) to data tools like Alkemi (“Which region grew fastest this quarter?”).

Conversational AI bridges the gap between people and systems, making technology feel intuitive, rather than technical.

Data Leakage

When private or confidential information accidentally leaves your environment — through uploads, emails, or public AI tools. That could mean pasting client data into a chatbot or exporting reports to personal drives.

Alkemi prevents leakage by isolating every query inside your secure system.

Training

Training is how AI systems learn patterns from data — it’s what turns a blank model into something that can understand language, recognize images, or make predictions.

There are two main types:

  • Pre-training: When a model learns general knowledge from huge text or data collections — understanding language, facts, and relationships.
  • Fine-tuning: When a model is later refined on specific examples to behave a certain way (for example, using a company’s support tickets to sound like its helpdesk).

Benefits:
Training allows AI systems to specialize, improve accuracy, and adapt to new industries or domains. A fine-tuned model can perform niche tasks, use brand tone, or understand company-specific data faster than a general model.

Risks:
If training data includes private, biased, or unverified information, the model can “learn” and repeat those issues. In extreme cases, sensitive data may be memorized or exposed through generated outputs.

Alkemi avoids those risks by never training or fine-tuning on customer data. Instead, it connects securely to governed data at the moment of inference, ensuring every answer is accurate, private, and up to date.

Agentic AI

AI that can take useful actions, not just answer questions — like generating a report, creating a chart, or automating a workflow. Agentic AI powers many modern business tools, from marketing automations to internal assistants.

Alkemi’s DataLab applies this principle to make data exploration faster and more interactive.

Enterprise AI

AI designed for real organizations — secure, compliant, and scalable across teams. Unlike consumer tools, enterprise AI respects your company’s privacy, governance, and compliance boundaries, ensuring innovation happens safely and responsibly.

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In Summary

Understanding these terms helps your team use AI responsibly — and confidently.
When AI runs on governed, verified data, you get smarter answers, not bigger risks.

Learn more about how Alkemi connects AI to governed data →

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