The AI terms for business owners come down to about 20 words. Once you know what an LLM, a token, RAG, an AI agent and a pilot are, you can follow almost any vendor call and spot a vague answer. Each term below gets one or two lines and an example from an Indian office.
This list is for owners of clinics, schools, shops, factories and agencies who hear these words on calls and nod along. Save the table in the middle.
Key takeaways
Most AI products you will be sold are built on a handful of ideas: a model, your data, a prompt and a check by a human.
An LLM predicts text. It does not look things up unless someone connects it to your documents, which is what RAG does.
Accuracy and hallucination are the two words that decide whether an AI tool is safe to use with customers.
A pilot is a small paid test on your own data. Ask for one before any large commitment.
What are the basic AI terms for business owners to know first?
These six words appear in almost every conversation, so start here.
1. Artificial intelligence (AI)
AI is software that does a task that normally needs human judgement, such as reading a document or answering a question. It is a broad label, not one technology. When someone says "we use AI", ask which task it does.
2. Machine learning (ML)
ML is a way of building software where the computer learns patterns from examples
instead of following rules a developer wrote by hand. A tool that learns to flag late-paying students from past fee records is machine learning.
3. Model
A model is the trained "brain" that takes an input and gives an output. A model that reads a photo of an invoice and returns the amount is one model.
4. Large language model (LLM)
An LLM is a model trained on huge amounts of text so it can read and write language. ChatGPT, Gemini and Claude are all products built on LLMs. An LLM predicts the next likely words, which is why it can write well and still be wrong.
5. Prompt
A prompt is the instruction you give an LLM. "Summarise this patient history in five lines" is a prompt.
6. Token
A token is a small chunk of text, roughly a word or part of a word, that an LLM reads and writes. AI providers usually charge by the number of tokens used, so long documents and long chats cost more. Our post on AI chatbot running cost in India explains how this shows up on a monthly bill.
What do training, fine-tuning and inference mean?
Training is how a model learns, fine-tuning is how it is adjusted for your work, and inference is the model doing its job once it is built.
7. Training data
Training data is the set of examples a model learns from. If you want a model to read your clinic's lab reports, the training data is a few hundred real reports with the right answers marked.
8. Fine-tuning
Fine-tuning means taking an existing model and training it a little more on your own examples so it behaves the way you want. It is useful for a fixed style or a narrow task. Many businesses do not need it.
9. Inference
Inference is the moment the model is used: you send an input and it returns an answer. Every WhatsApp reply from an AI assistant is one inference. Running costs come from inference, not from training.
10. Accuracy
Accuracy is how often the model gets the answer right on test data it has not seen before. If a vendor says "95% accurate" , ask on whose data and how it was measured. A fair number comes from your documents, not from a demo set.
Which terms matter when AI uses my own documents?
Three terms matter most here: RAG, embeddings and vector databases, and they work together to let an AI answer from your files.
11. RAG (retrieval-augmented generation)
RAG means the system first finds the right pages from your documents and then asks the LLM to answer using only those pages. It is how a chatbot can answer from your fee policy or product catalogue. Our plain-English guide to RAG goes deeper.
12. Embedding
An embedding is a list of numbers that captures the meaning of a piece of text. Two sentences with similar meaning get similar numbers, even if the words differ. This is how a search for "refund" can also find a page about "money back" .
13. Vector database
A vector database stores embeddings so the system can find similar meaning quickly. Think of it as a very good index for your documents.
14. Hallucination
A hallucination is when an LLM states something confidently that is false. It might invent a fee amount or a clause that does not exist. RAG, clear instructions and a human check reduce hallucinations, but nobody removes them completely.
What do chatbot, AI agent and API mean?
A chatbot answers, an AI agent acts, and an API is the connection that lets software talk to other software.
15. Chatbot
A chatbot is a program that holds a conversation, on a website or on WhatsApp.
16. AI agent
An AI agent is an LLM connected to tools so it can do steps, such as checking a calendar, booking a slot and sending a confirmation. It is more useful than a chatbot and also needs more care, because it takes actions. Read AI agents vs AI chatbots to see the difference with examples.
17. API
An API is a doorway that lets one piece of software ask another for something. Your billing software sending a payment request to Razorpay or a WhatsApp message through a provider happens through APIs. Most AI features reach your system through an API.
18. OCR (optical character recognition)
OCR turns the text in a scanned image or PDF into text a computer can use. It is the firststep when reading invoices, KYC documents or GST bills. See AI document processing for invoices, KYC and GST.
Which terms help me judge cost and risk?
Two terms protect your budget and your customers: pilot and human in the loop.
19. Pilot
A pilot is a small, time-boxed test on your real data to measure whether the AI works before you build the full system. At Ureka, every AI project starts with a fixed-price pilot of about 4 weeks. If the pilot shows AI is the wrong tool, you find out cheaply.
20. Human in the loop
Human in the loop means a person reviews the AI's output before it reaches a customer or changes a record. For fee notices, medical summaries or legal drafts, it is the safest setting to begin with.
Term | One-line meaning |
AI | Software doing a task that needs judgement |
Machine learning | Learning patterns from examples |
Model | The trained brain for one task |
LLM | Model that reads and writes language |
Prompt | The instruction you give |
Token | Chunk of text AI is billed on |
Training data | Examples a model learns from |
Fine-tuning | Extra training on your examples |
Inference | The model giving an answer |
Accuracy | How often it is right on new data |
RAG | Answer from your documents |
Embedding | Numbers that capture meaning |
Vector database | Index of embeddings |
Hallucination | Confident wrong answer |
Chatbot | Program that converses |
AI agent | LLM that takes actions |
API | Software-to-software doorway |
OCR | Image to text |
Pilot | Small paid test on your data |
Human in the loop | Person checks the output |
What should I ask a vendor using these terms?
Ask four questions: what task does the model do, what data will it use, how will accuracy be measured on my documents, and who checks the output.
If your data includes customer details, also ask where it is stored and who owns it. Our guide to DPDP compliance for website owners covers the basics of handling personal data in India.
When we scope AI work, the part that usually takes longest is not the model. It is getting clean, labelled examples from the client's own files. If you want to see how Ureka approaches this, our AI and machine learning development page explains the pilot-first process.
FAQ
What is the difference between AI and machine learning?
AI is the broad goal of software doing tasks that need judgement. Machine learning is one way to get there, where software learns patterns from examples instead of fixed rules. Most AI tools a business buys today use machine learning underneath, so the two words are often used together.
What is a token in AI and why does it affect cost?
A token is a small chunk of text, roughly a word or part of a word, that an AI model reads or writes. Providers usually charge per token, so longer prompts, long documents and long conversations cost more. Keeping instructions short and sending only the relevant pages keeps the bill lower.
What is a hallucination in AI?
A hallucination is when an AI model gives a confident answer that is false, such as an invented fee amount or a rule that does not exist. It happens because the model predicts likely text. Connecting it to your documents with RAG, and having a person review important outputs, reduces it.
Do I need fine-tuning or RAG for my business?
Most businesses that want an AI to answer from their own documents need RAG, not fine-tuning. RAG finds the right pages and answers from them, so updates to your documents show up straight away. Fine- tuning suits a fixed style or a narrow task and needs good example data.
What is an AI pilot?
An AI pilot is a small, time-boxed test on your own data to measure accuracy before you build the full system. At Ureka it is a fixed-price project of about 4 weeks. It shows whether AI works for your task, so you do not pay for a full build on a guess.