Artificial Intelligence (AI) is transforming our world, driving innovations in diverse areas like conversational agents, personalized recommendations, artistic creation, and self-operating systems. For newcomers, the rapid evolution of AI can be a bit daunting, with specialized terms such as transformers, embeddings, RAG, and fine-tuning appearing frequently. This comprehensive guide aims to demystify 20 core AI concepts, presenting them in an accessible and straightforward manner.

1. Large Language Models (LLMs)

Leading the charge in the current AI landscape are LLMs, including popular examples like GPT, Claude, and Llama. These are sophisticated neural networks designed with a primary goal: to accurately predict the next word in a sequence. For instance, given “All that glitters…”, an LLM would likely complete the phrase with “…is not gold.” This seemingly simple predictive ability forms the foundation of their remarkable intelligence.

2. Tokenization

Before any text data can be processed by an AI model, it undergoes a crucial step called tokenization. This involves breaking down the text into smaller, manageable units known as tokens, which can be individual words, parts of words, or even punctuation marks. For example, “dancing” might be tokenized into [“danc”, “ing”]. This process provides the model with a structured, granular view of the language it’s working with.

3. Vectors (Embeddings)

Once text is tokenized, AI models transform these tokens into numerical representations called vectors, also known as embeddings. These vectors are positioned in a multi-dimensional space such that words with similar meanings are located closer to each other. For instance, “happy” and “joy” would be numerically close, while “sad” would be further apart. This numerical mapping is how AI models grasp the semantic relationships and meaning behind words.

4. Memory

The concept of memory in LLMs enables them to recall and utilize information from past interactions within a conversation. This feature allows AI systems to maintain context, remember user preferences, and retain specific facts, leading to more personalized and coherent exchanges over extended periods. Try Langbase Memory.

5. Self-Supervised Learning

Unlike traditional supervised learning that relies on human-labeled datasets, self-supervised learning allows models to learn autonomously. They achieve this by filling in blanks, such as predicting missing parts of data: “All that glitters ___ not gold.” This method is highly scalable, enabling models to process vast amounts of data (trillions of tokens) without the need for extensive manual annotation.

6. Tools

In the context of LLMs, tools refer to external functions or Application Programming Interfaces (APIs) that the model can access and utilize. These tools significantly broaden the capabilities of an LLM, allowing it to perform actions beyond text generation, such as retrieving real-time information or interacting with databases. Essentially, tools act as the model’s interface to interact with the outside world. To learn about tools in detail, see this guide.

7. Fine-Tuning

While a base AI model is designed for general purposes, fine-tuning involves further training this model on a specific, specialized dataset. This data could pertain to a particular domain like legal, medical, or financial information. The outcome of fine-tuning is a model that is highly optimized and adapted to perform tasks within that specific domain with greater accuracy and relevance.

8. Few-Shot Prompting

Rather than undergoing extensive retraining, few-shot prompting is a technique where an AI model is given a few examples within the prompt itself to guide its response. By observing these examples, the model can quickly grasp the desired output format or style, such as understanding the expected answer style for a query like “Where’s my parcel?”

9. Retrieval-Augmented Generation (RAG)

LLMs typically lack access to real-time information. RAG addresses this limitation by integrating a retrieval component. It works by: 1) fetching relevant documents or data from an external knowledge base or database, 2) feeding this retrieved information to the LLM, and 3) enabling the model to generate responses that are accurate and grounded in up-to-date facts. RAG is a foundational technology for many modern AI applications requiring current information. To learn about RAG in detail, see this guide.

10. Vector Databases

Essential for implementing RAG, vector databases are specialized data stores designed to efficiently manage and search document embeddings (vectors). Their primary function is to quickly identify and retrieve the most relevant documents based on semantic similarity, which is crucial for providing contextual information to LLMs. Examples: Pinecone, Weaviate, Milvus, FAISS.

11. Model Context Protocol (MCP)

LLMs are inherently designed for language generation and do not possess autonomous browsing or action capabilities. The Model Context Protocol (MCP) serves as a bridge, enabling LLMs to connect with and leverage external tools and APIs. This empowers them to perform complex actions, such as “Book me a flight tomorrow,” by interacting with real-world services. Try building MCP-powered agents with Langbase.

12. Context Engineering

Moving beyond simple prompt engineering, context engineering involves the meticulous preparation and structuring of all information provided to an LLM. This includes strategies like RAG, few-shot examples, summarization techniques, and integrating external tools. The ultimate objective is to ensure the LLM receives precisely the right context at the optimal moment to generate accurate and relevant outputs.

13. Agents

AI agents are sophisticated, LLM-driven programs capable of a wider range of actions than just generating text. They can autonomously utilize tools, interact with APIs, and coordinate multiple tasks. Instead of merely answering questions, agents can plan, retrieve necessary data, and execute actions, much like a travel agent who can find flights, book hotels, and send you an itinerary. Try building one with Langbase runtime agents.

14. Reinforcement Learning from Human Feedback (RLHF)

RLHF is a critical method for aligning AI models with human values and preferences. It involves having the model produce several responses, which are then evaluated and ranked by human reviewers. Positive feedback for good answers reinforces those behaviors, while negative feedback for undesirable ones helps the model learn to avoid them. Through this iterative process, the model learns human preferences.

15. Chain of Thought (CoT)

Chain of Thought prompting is a technique where an AI model is encouraged to articulate its reasoning process step-by-step, rather than just providing a direct answer. This approach significantly enhances the model’s ability to tackle complex tasks, including mathematical problems, logical puzzles, and intricate problem-solving scenarios, by making its thought process explicit.

16. Multimodal Models

Representing a significant leap forward in AI, multimodal models are designed to process and understand various types of data inputs simultaneously, including text, images, audio, and video. This capability allows for diverse applications, such as summarizing information from an uploaded chart, generating music based on a prompt, or creating variations of an image from a text description.

17. Small Language Models (SLMs)

In contrast to their larger counterparts, Small Language Models (SLMs) are compact, often domain-specific AI models. They are designed to be more efficient in terms of computational resources and cost-effectiveness. SLMs are particularly well-suited for businesses requiring private, optimized AI solutions for specific tasks without the overhead of massive general-purpose models.

18. Distillation

Model distillation is a technique used to create smaller, more efficient AI models without sacrificing significant performance. It involves training a compact “student” model to replicate the behavior and outputs of a larger, more complex “teacher” model. This process results in a lighter and faster model, making it more practical for deployment in resource-constrained environments.

19. Reasoning Models

Going beyond mere prediction, reasoning models imbue AI with the ability to engage in complex cognitive processes such as planning, deconstructing problems into smaller parts, and exploring various potential solutions. These models represent AI systems that can think and strategize, rather than simply generating outputs based on learned patterns.

20. Foundation Models

Foundation models are large-scale AI models trained on massive and diverse datasets, serving as the initial base layers for many specialized AI applications. These powerful base layers can then be adapted, fine-tuned, or integrated by developers to create more focused and efficient models for a wide array of specific tasks and domains.

Final thoughts

In conclusion, while the field of Artificial Intelligence can often seem complex, its fundamental principles revolve around recognizing patterns, understanding context, and executing logical reasoning. By grasping these 20 core concepts, you will establish a strong foundation to delve further into AI, whether your interest lies in developing AI products, conducting research, or simply exploring the transformative technologies shaping our collective future.