AI tools change quickly. A product that leads the market today may be replaced by a faster or cheaper option within months. The technology concepts behind these products usually remain useful for much longer. This guide explains 20 widely used AI terms in plain English, along with the tools and technologies commonly linked to each one.
1–2. Vector databases and large language models
1. Vector Database — A vector database stores data as numerical representations called vectors. Traditional search often looks for matching words, while vector search looks for similarity in meaning. A search for ‘AI course for working managers’ may therefore find a page titled ‘Generative AI program for business leaders’ even when the exact words do not match. Pinecone, Weaviate, Qdrant, Milvus, Chroma, pgvector and FAISS are commonly linked to vector search.
2. Large Language Model (LLM) — An LLM is trained on a very large collection of text and other data. It can interpret instructions and produce language-based output. LLMs power chat assistants, writing tools, coding systems, research applications and many business AI products. GPT, Claude, Gemini, Llama, Qwen, Mistral and DeepSeek are well-known model families. The LLM may act as the language layer of an application, but it often needs data, tools and controls around it for business use.
The next step is not simply to learn another AI tool. It is to learn how the parts behind the tool connect.
3–4. Embeddings and RAG
3. Embedding Model — An embedding model converts text, images or other information into numerical representations called embeddings. Items with related meanings usually produce vectors located closer together in mathematical space. This lets a system compare meaning instead of depending only on exact keywords. OpenAI embedding models, Gemini embeddings, Voyage AI, Cohere Embed, BGE, E5 and Sentence Transformers are common examples.
4. Retrieval-Augmented Generation (RAG) — RAG lets an AI model fetch outside information before writing an answer. If an employee asks about a company leave policy, a RAG system can search the latest HR documents and pass the best passages to the LLM. A basic flow is: question, embedding, search, relevant content, LLM and answer. LangChain, LlamaIndex, Haystack, embedding models, vector databases and rerankers are often used in these systems.
- RAG is useful for private company information
- It can work with frequently updated material
- The retrieved evidence still needs quality checks
5–6. AI agents and orchestration
5. AI Agent and Agentic AI — A basic chatbot replies to a request. An AI agent can work through a goal using several steps. Depending on its permissions, an agent may choose a tool, search records, call an API, compare results and take an action. OpenAI Agents SDK, LangGraph, CrewAI, AutoGen and Google ADK are used to build agent systems. The main difference is action: an agent is not limited to generating text.
6. AI Orchestration Framework — Production AI applications may combine prompts, databases, APIs, tools, memory and business rules. An orchestration framework coordinates these parts and defines the order in which they run. LangChain, LangGraph, LlamaIndex, Semantic Kernel and Haystack are common choices. A workflow might classify a customer question, search the correct database, call an LLM and route high-risk cases to a person.
7–8. Inference engines and AI gateways
7. Inference Engine and Model Serving — Training produces an AI model. Inference is the process of using that trained model to generate a prediction or response. Model-serving systems make models available to applications and help manage speed, memory use, batching and hardware. vLLM, TensorRT-LLM, Ollama, llama.cpp, Hugging Face TGI and NVIDIA Triton are common technologies. Inference matters when a company wants to run an open model privately or control computing costs.
8. AI Gateway or LLM Gateway — Many companies use more than one model provider. An AI gateway provides a common layer between an application and different model services. It can manage routing, authentication, rate limits, logs, fallbacks and usage tracking. LiteLLM, Portkey, OpenRouter, Kong AI Gateway and Cloudflare AI Gateway are examples. A gateway can make it easier to switch providers without rewriting every part of an application.
9–10. LLMOps and knowledge graphs
9. MLOps and LLMOps — Creating an AI application is only one part of the work. Teams must deploy it, test it, watch performance and track changes. MLOps covers the operational side of machine-learning systems, while LLMOps applies related practices to language-model applications. MLflow, Weights & Biases, LangSmith, Arize Phoenix, Evidently and Datadog can track model versions, response quality, latency, failures, usage and cost.
10. Knowledge Graph — A knowledge graph stores facts through entities and relationships. For example: a student is enrolled in an AI course; the course is taught by a trainer; and the trainer specialises in generative AI. This helps a system answer questions based on connected facts rather than isolated documents. Neo4j, Amazon Neptune, ArangoDB, TigerGraph, RDF and SPARQL are commonly used. Knowledge graphs can also be combined with RAG.
11–12. Fine-tuning and multimodal AI
11. Fine-Tuning — Fine-tuning takes an existing model and trains it further using selected examples. It may teach a model a response format, tone, vocabulary or task pattern. It differs from RAG: RAG supplies information when a request is made, while fine-tuning changes model behaviour through additional training. OpenAI fine-tuning, Hugging Face PEFT, LoRA, QLoRA, Axolotl and Unsloth are common technologies. Better prompts, structured outputs or RAG may sometimes solve the task with less maintenance.
12. Multimodal AI — Multimodal AI can work with more than one kind of information, such as text, images, audio, video and documents. A multimodal assistant may read a chart, answer a spoken question, inspect a product photo or summarise a video. Multimodal models exist in the GPT, Gemini, Claude and Qwen families, alongside open vision-language models such as LLaVA.
13–14. Foundation models and SLMs
13. Foundation Model — A foundation model is broadly trained and can serve as a base for many applications and tasks. Instead of training a new model from the beginning for every use case, developers can connect a foundation model to their data, instructions and tools. GPT, Gemini, Claude, Llama and Qwen are widely used foundation-model families.
14. Small Language Model (SLM) — Not every task requires a very large model. An SLM uses fewer computing resources and may run on a local server, laptop, phone or edge device. Microsoft Phi, Gemma, smaller Qwen models and smaller Llama variants are examples. SLMs may suit narrow tasks, on-device work, lower-cost processing and cases where data should remain within a company’s environment.
15–16. Tool calling and MCP
15. Function Calling or Tool Calling — A language model mainly produces output. Tool calling lets it request an action from outside software. If a user asks which customer orders are delayed today, the model cannot know the current answer on its own. It can request data from an order-management API and use the returned records to answer. Tool calling connects AI models with CRMs, calendars, payment systems, databases and internal applications.
16. Model Context Protocol (MCP) — MCP is an open standard for connecting AI applications with external tools, data and workflows. Without a shared protocol, developers may need a separate custom integration for every pairing of an AI application and a business tool. MCP provides a common method for compatible systems to discover and use those connections. Permissions and security rules still require careful design.
17–18. Guardrails and reranking
17. AI Guardrails — When an AI system can access company data or perform actions, it needs limits. Guardrails can check user input, model output and proposed actions. They may block prohibited content, enforce a response format, protect sensitive data or stop an agent from acting outside its permission. Guardrails AI, NeMo Guardrails, Llama Guard and moderation systems are common examples. Guardrails do not replace testing, access control or human review in high-risk work.
18. Reranking — A search system may retrieve 20 possible documents, but the first result is not always the best. A reranker reviews those candidates and orders them according to relevance. The flow becomes: question, vector search, candidate documents, reranker, best documents and LLM. Cohere Rerank, Voyage rerankers, BGE Reranker, Jina rerankers and cross-encoder models are often used.
19–20. Semantic search and AI memory
19. Semantic Search — Traditional search often depends on matching words. Semantic search attempts to match meaning. A person searching for ‘How can I become an AI developer?’ may receive a page titled ‘Career Path for Generative AI Engineers’ even though the wording is different. Semantic search commonly uses embeddings and vector similarity. Hybrid search combines semantic and keyword methods.
20. AI Memory and Context Management — An AI assistant may need information from an earlier conversation or a previous interaction. AI memory refers to methods used to store and retrieve that information. A system might use the active conversation window, a structured database, vector storage or a purpose-built memory service. Mem0, Zep, LangGraph memory, Redis, PostgreSQL and vector databases are common choices. Good memory design also requires rules for consent, privacy, retention and forgetting.
How the complete AI stack fits together
Imagine a company building an internal assistant for thousands of documents and several business applications. An embedding model converts document sections into vectors, a vector database stores them, semantic search finds related material and a reranker places the best matches first. RAG sends this material to an LLM, while memory retains permitted context.
An AI agent decides whether another step is needed. Tool calling lets it interact with business software, and MCP may provide a standard connection method. An orchestration framework manages the workflow, guardrails check permissions and actions, and LLMOps tools track quality, speed, failures and cost. A modern AI application is rarely just an LLM; it is a stack of connected parts.
Beginners can start with six terms: LLM, embeddings, vector database, semantic search, RAG and tool calling. Next, study agents, MCP, guardrails and orchestration. Fine-tuning, inference and LLMOps become more useful when you begin building or running production systems.
Common questions
Do I need coding knowledge to learn these AI terms?
No. You can first learn what each technology does and where it is used. Coding becomes necessary when you want to build, customise or deploy applications.
What is the difference between an LLM and an AI agent?
An LLM produces language-based output. An AI agent uses a model as part of a system that can plan steps, select tools and perform permitted actions.
What is the difference between RAG and fine-tuning?
RAG retrieves information when a question is asked. Fine-tuning trains an existing model further to change how it responds or performs a task.
Are vector databases only used for RAG?
No. They are also used for semantic search, recommendations, matching, classification support and duplicate detection.
Why is MCP receiving so much attention?
MCP gives compatible AI systems a standard way to connect with tools and data. This can reduce the need to build every integration from the beginning.