The AI Glossary for Small-Business Leaders

AI is suddenly everywhere—inside your inbox, on your invoices, even in the apps your bookkeeper swears by. Yet the vocabulary that vendors and tech media use can feel like alphabet soup: LLMs, RAG, vectors, embeddings… If you don’t have a CTO on speed-dial, it’s hard to tell what’s hype, what’s relevant, and what’s worth paying for.

This glossary is your quick translator. In five minutes, you’ll know exactly what 15 of the most common AI terms really mean, why they matter to day-to-day business, and how to spot them in sales pitches—without getting lost in math or code. Bookmark it, share it with your team, and refer back any time an “AI solution” crosses your desk.

1. Generative AI

Algorithms that create new content—text, images, audio, or code—after learning patterns from huge data sets. Think ChatGPT writing an email draft or DALL-E sketching product mock-ups.

2. Large Language Model (LLM)

A supersized neural network trained on trillions of words that can answer questions, summarize documents, or carry on a conversation in natural language. This is the foundation of popular tools like ChatGPT. This is a specific type of generative AI.

3. Natural Language Processing (NLP)

The broad AI field that helps computers understand, interpret, and generate human language, powering chatbots, voice assistants, and auto-translation.

4. Prompt Engineering

The craft of wording a question or command so a generative AI model gives the result you need—similar to writing a great search query, but more structured. LLMs, for example, require the user to “prompt” them with input text in order for them to respond. Prompt Engineering can be thought of as a way to improve your input to increase the likelihood of getting your desired output.

5. Retrieval-Augmented Generation (RAG)

A workflow that lets an LLM “look up” fresh, authoritative data (say, your knowledge base) before it answers, reducing hallucinations and adding citations. For example, ChatGPT may use RAG to search the web or your own documents to give it extra context to add to its response.

6. Vector Database

A specialized datastore that stores each document, image, or clip as a high-dimensional numeric “fingerprint” (vector), letting it retrieve the closest matches in milliseconds—vital for semantic search and RAG. Example: Type “cat” and it shows kittens, lion cubs, and feline-care images even if the word “cat” never appears in the files.

7. Embeddings

Those numeric fingerprints themselves: multi-dimensional number arrays capturing meaning or context, e.g., turning a sentence into a 1,536-element vector the model can compare.

8. No-Code AI

Drag-and-drop platforms that let non-programmers build AI workflows—think uploading spreadsheets, choosing a template, and clicking “Run”. These platforms are becoming more and more powerful (Zapier, Gumloop, etc.) but may not be suitable for fully custom workflows.

9. Computer Vision

AI that “sees” images or video: spotting defects on an assembly line, counting visitors in a store, or tagging products in photos. Modern LLMs can utilize this to understand documents, categorize images, and more.

10. Sentiment Analysis

Software that gauges positive, negative, or neutral feelings in reviews, social posts, or support tickets—handy for live customer-service dashboards.

11. Synthetic Data

AI-generated data that mimics real records but contains no personal details—useful for training models when privacy or scarcity blocks access to originals.

12. Data Labeling

The grunt work of tagging images, text, or audio so supervised models have “ground truth” to learn from—often outsourced or semi-automated. As LLMs get bigger and bigger, there is a huge appetite for well labeled, unique data to help them get smarter.

13 Fine-Tuning

Taking a big pre-trained model and giving it a second round of training on your data so it speaks your jargon or follows your policies. This can be especially helpful for businesses with a large amount of internal data or who need to follow specific guidelines. You can essentially tune the LLM to better fit your use case or teach it a new skill.

14. Hallucination

A confident-sounding answer that’s flat-out wrong—common in generative models when they can’t find grounding data. RAG and other techniques can help mitigate this but it’s important to fully test and verify any AI system before letting it handle real use cases.

15. Pre & Post Training

The two-step schooling of an AI model. Pre-training gives it a broad education on internet-scale data so it learns general language skills. Post-training (Fine-tuning is one example of this) is the on-the-job phase where you feed in your own examples and rules to teach it how to respond or think through problems.