Terms You Should Know in the World of Generative Artificial Intelligence

CONVERSATIONAL AI

Understanding Concepts Correctly Is the First Step to Using Them Properly

Generative artificial intelligence is not only developing new technologies but also fundamentally transforming the way we work. It lies at the heart of transformation in many areas—from customer service and software development to content creation and product design. However, to benefit effectively from this transformation, it is essential to understand the concepts correctly. At CBOT, we have compiled the key concepts we frequently encounter when discussing generative AI, using current terminology and a language suited to the business world.

Generative Artificial Intelligence (GenAI)

A type of artificial intelligence capable of learning patterns in data and generating text, visuals, audio, or code. Based on the datasets it is trained on, it produces original and contextually appropriate content. GenAI enables companies to redefine customer experience, automate processes, and create new areas of value.

Large Language Model (LLM)

Known as “Large Language Model,” these systems are trained on massive text datasets. They stand out with their natural language understanding and generation capabilities. They form the foundation of many applications, such as chatbots, translation, and summarization. LLMs are core components of CBOT’s GenAI solutions.

Hallucination

When the model generates content that appears correct but is actually inaccurate or fabricated. This issue often arises from gaps in training data or failure to establish proper context. If not controlled, hallucination risks can cause trust issues in business processes. For this reason, at CBOT, we constantly test the accuracy of our responses.

Prompt and Prompt Engineering

“Prompt” refers to the input text that guides how the model responds. Prompt engineering is the process of designing these inputs to yield accurate and effective results. Prompt templates, prompt chaining, and prompt tuning are key technical components of this process.

Retrieval-Augmented Generation (RAG)

RAG is a method that allows models to access external data sources to produce more accurate and up-to-date responses. It plays a critical role especially in enterprise use cases where models need to be supported by internal company knowledge. This is a frequently used approach in CBOT’s enterprise solutions.

Training and Inference

Training refers to the process where a model learns from historical data. Inference is how the trained model responds to or makes predictions about new data. While training is resource-intensive, inference usually works in real time. At CBOT, we manage this distinction carefully to optimize both efficiency and performance.

Embedding

A method that transforms data into numerical vectors for meaningful representation. This allows the system to easily find, match, or suggest similar content. It plays a critical role in search engines and recommendation systems. Embeddings are a foundational element of CBOT’s search-driven solutions.

Mixture of Experts (MoE)

Within the model, there are sub-models (experts) specialized in different tasks. A gating network determines which expert to activate. This increases performance and ensures efficient resource use. It is highly suitable for large and complex workloads.

LoRA Fine-Tuning

Low-Rank Adaptation (LoRA) allows for quick and efficient customization of large models with small datasets. Instead of altering the entire model, it applies low-rank adaptations to specific parts. This significantly improves cost and process efficiency, especially in enterprise-specific AI applications.

Diffusion Models

A machine learning technique primarily used for visual generation. Starting with random noise, the process refines it step by step to produce original and high-quality visuals. It is the core technology behind systems like DALL·E and Stable Diffusion.

Transformer and Token

The transformer is a neural network architecture capable of processing sequential data while maintaining context via an attention mechanism. It forms the basis of large language models like GPT. A token is the smallest unit of language the model processes. The temperature parameter determines how creative or consistent the model’s responses will be.

These concepts are central for anyone working with generative AI. Especially at the enterprise level, understanding not just the technology but also its terminology is critical to leveraging it effectively. At CBOT, we consider mastering this terminology a fundamental responsibility in all our solutions to ease our customers’ journeys.