20 AI Terms Every Developer Should Know

AI moves fast.

Over the past year we've been hearing terms like:

• RAG
• MCP
• Reasoning models
• Fine-tuning
• Distillation
• Agents
• Vector databases

And if you’re building AI applications, understanding these concepts matters.

This guide breaks down 20 important AI terms in simple language so you can better understand how modern AI systems actually work.

1. Large Language Model (LLM)

A Large Language Model is an AI model trained to predict the next word or token in a sequence.

For example:

The model predicts:

Models like:

• GPT
• Claude
• DeepSeek
• Qwen

are all examples of LLMs.

2. Tokenization

Before an LLM understands text, it breaks the input into smaller pieces called tokens.

Example:

might become:

• play
• ing

This process is called tokenization.

Tokens help models understand language patterns more efficiently.

3. Vectors

AI models don’t understand words directly.

They convert words into numerical representations called vectors.

Vectors help models understand:

• meaning
• similarity
• relationships between words

For example:

• cat
• dog
• tiger

may exist close together in vector space because they’re semantically related.

4. Attention

Attention is what allows AI models to understand context.

Example:

vs

The word “apple” means different things depending on nearby words.

Attention helps models focus on relevant context to determine meaning.

This was one of the biggest breakthroughs in modern AI.

5. Self-Supervised Learning

Self-supervised learning means models learn from the structure of data itself.

Instead of humans manually labeling everything, models learn by predicting missing parts.

Example:

The model learns to predict:

This made training large AI systems massively scalable.

6. Transformer

A transformer is the architecture behind most modern LLMs.

It uses:

• attention
• neural networks
• stacked processing layers

to understand and generate language.

GPT models are transformer-based models.

7. Fine-Tuning

Fine-tuning takes a base model and trains it further for a specific task.

For example:

• medical AI
• legal AI
• coding AI
• finance AI

A fine-tuned model becomes specialized in a particular domain.

8. Few-Shot Prompting

Few-shot prompting means giving examples inside the prompt.

Example:

The model learns the expected response pattern from examples.

9. Retrieval-Augmented Generation (RAG)

RAG improves AI responses by adding external documents into the model context.

Instead of relying only on training data, the system retrieves:

• company docs
• policies
• knowledge bases
• PDFs

and sends them with the prompt.

This makes responses more accurate and company-specific.

10. Vector Database

A vector database stores vector embeddings for efficient similarity search.

It helps AI systems find:

• relevant documents
• similar content
• related context

Popular vector databases include:

• Pinecone
• Weaviate
• Chroma

11. Model Context Protocol (MCP)

MCP allows AI models to connect with external tools and systems.

Instead of only generating text, models can:

• query APIs
• fetch real-time data
• execute workflows
• interact with tools

This makes AI agents significantly more powerful.

12. Context Engineering

Context engineering is the process of managing what information gets sent to an AI model.

This includes:

• prompt design
• memory
• retrieved documents
• chat history
• tool outputs

Good context engineering often matters more than the model itself.

13. Prompt Engineering

Prompt engineering is designing prompts to improve model outputs.

Example:

• adding instructions
• defining tone
• specifying formats
• providing examples

Prompt engineering focuses on a single interaction.

14. AI Agents

Agents are AI systems that can:

• reason
• plan
• use tools
• take actions
• complete workflows autonomously

Instead of answering one prompt, agents operate continuously toward goals.

Examples:

• coding agents
• travel agents
• research agents

15. Reinforcement Learning

Reinforcement learning improves models using rewards and penalties.

Good outputs receive positive feedback.

Bad outputs receive negative feedback.

This helps models optimize behavior over time.

A common version is:

which means:

16. Chain of Thought

Chain of thought prompting encourages models to reason step-by-step.

Instead of giving immediate answers, the model breaks problems into smaller logical steps.

This improves reasoning quality significantly.

17. Reasoning Models

Reasoning models are AI systems optimized for multi-step problem solving.

These models:

• think longer
• evaluate intermediate steps
• solve harder problems

Examples include:

• DeepSeek R1
• OpenAI o-series models

18. Multimodal Models

Multimodal models can work with multiple input types.

Not just text.

They can process:

• images
• audio
• video
• screenshots

Examples:

• GPT-4o
• Gemini
• Claude multimodal systems

19. Small Language Models (SLMs)

Small Language Models are lightweight AI models with fewer parameters.

They are:

• cheaper
• faster
• easier to run locally

Companies often use SLMs for:

• internal tools
• customer support
• specialized workflows

20. Distillation and Quantization

Distillation

Distillation transfers knowledge from a large model into a smaller model.

The smaller model learns to imitate the larger one.

This reduces infrastructure costs significantly.

Quantization

Quantization reduces model memory usage by compressing numerical precision.

Example:

• 32-bit weights → 8-bit weights

This makes models:

• faster
• cheaper
• easier to deploy

especially on local hardware.

Wrap Up

AI terminology can feel overwhelming because the industry evolves so quickly.

But most modern AI systems are built from a combination of the same core ideas:

• transformers
• vectors
• attention
• context
• reasoning
• retrieval
• orchestration

Once you understand these terms, it becomes much easier to:

• build AI applications
• understand AI tooling
• evaluate model claims
• follow industry trends

And honestly:

A lot of AI hype starts sounding much less unreal once you understand the underlying concepts.