Artificial Intelligence (AI) has exploded in recent years, powering everything from chatbots and recommendation engines to image generators and autonomous agents.
But if you’re just starting out, the jargon can feel overwhelming.
Terms like transformers, embeddings, RAG, and fine-tuning pop up everywhere.
This guide breaks down 20 fundamental AI concepts in plain language.
1. Large Language Models (LLMs)
At the core of today’s AI revolution are LLMs like GPT, Claude, and Llama.
They’re essentially giant neural networks trained to predict the next word.
Input: “All that glitters…”
Output: “…is not gold.”
That simple predictive mechanism unlocks surprising intelligence.
2. Tokenization
Before text can be fed into a model, it’s chopped into tokens (small chunks of text).
Example: “dancing” → [“danc”, “ing”]
This allows the model to work with language at a granular, structured level.
3. Vectors (Embeddings)
Models convert tokens into vectors, numerical points in multi-dimensional space.
Words with similar meanings end up close together.
- “happy” sits near “joy”
- “sad” is further away
This is how AI “understands” meaning.
4. Memory
Memory lets LLMs retain information from past interactions.
It helps them remember context, preferences, or facts across conversations.
This makes AI more personal and consistent over time.
Try Langbase Memory
5. Self-Supervised Learning
Instead of humans labeling data, models learn by filling in blanks:
“All that glitters ___ not gold.”
This self-supervised method scales to trillions of tokens, no need for endless manual labeling.
6. Tools
Tools are external functions or APIs that an LLM can call.
They extend what the model can do, like fetch data, or query databases.
Think of them as the model’s “hands” to interact with the world.
To learn about tools in detail, see this guide.
7. Fine-Tuning
A base model is general-purpose.
Fine-tuning = retraining it on specialized data (legal, medical, financial, etc.).
The result: a model adapted to your domain.
8. Few-Shot Prompting
Instead of retraining, sometimes you just need to show examples in the prompt:
Q: “Where’s my parcel?”
Example → Answer style
The model learns to mimic the pattern instantly.
9. Retrieval-Augmented Generation (RAG)
LLMs don’t know real-time info.
RAG fixes this by:
- Retrieving relevant docs from a database
- Feeding them into the model
- Generating grounded answers
This is the backbone of many AI apps today. To learn about RAG in detail, see this guide.
10. Vector Databases
To power RAG, we need special databases.
They store document embeddings (vectors) and quickly find the most relevant ones.
Examples: Pinecone, Weaviate, Milvus, FAISS.
11. Model Context Protocol (MCP)
LLMs can’t browse or act on their own.
MCP lets them connect to external tools and APIs, extending their abilities.
Imagine asking an AI: “Book me a flight tomorrow.”
MCP makes it possible.
Try building MCP-powered agents with Langbase.
12. Context Engineering
Prompt engineering was just the beginning.
Context engineering means carefully shaping the information fed into an LLM:
- RAG
- Few-shot examples
- Summarization
- External tools
The goal: deliver the right context at the right time.
13. Agents
Agents are LLM-powered programs that can use tools, call APIs, and orchestrate tasks. They don’t just answer; they plan, fetch data, and take actions.
Example: a travel agent that:
- Finds flights
- Books hotels
- Emails your itinerary
Try building one with Langbase runtime agents.
14. Reinforcement Learning from Human Feedback (RLHF)
How do models become more human-aligned?
They generate multiple outputs.
Humans rate them.
- Good answers → rewarded
- Bad answers → penalized
Over time, the model learns human preferences.
15. Chain of Thought (CoT)
Instead of spitting out answers, models show step-by-step reasoning.
This helps with math, logic, and complex problem-solving.
16. Multimodal Models
The future isn’t just text.
Multimodal AI handles text, images, audio, and video.
You can:
- Upload a chart → get a summary
- Ask it to generate music
- Describe an image → get variations
17. Small Language Models (SLMs)
Not all models need to be massive.
SLMs are compact, domain-specific, and cheaper to run.
Perfect for enterprises that need private, efficient AI.
18. Distillation
How do you make big models smaller without losing smarts?
Distillation = training a small “student” model to mimic a large “teacher” model.
This makes deployment lighter + faster.
19. Reasoning Models
Beyond prediction.
Reasoning models can plan, break down problems, and explore solutions.
Think of them as AI that thinks more than just guesses.
20. Foundation Models
The giants that start it all.
Trained on massive datasets, they act as base layers.
From there, developers fine-tune or adapt them into specialized smaller models.
Final thoughts
AI can feel intimidating, but at its core, it’s about patterns, context, and reasoning.
If you understand these 20 concepts, you’ll have a strong foundation to explore deeper, whether you’re building AI products, researching, or just curious about the tech shaping our future.
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