Learn About AI

Translator prompts are specialized instructions designed to guide artificial intelligence systems in performing translation tasks with specific requirements for accuracy, cultural sensitivity, and contextual appropriateness.

Unsupervised learning is a type of machine learning where the AI model is given a dataset without any explicit instructions or labeled examples, and it must find the underlying structure, patterns, and relationships on its own.

User prompts are specific instructions, questions, or requests that individuals give to artificial intelligence systems to guide their responses or outputs. They serve as the primary interface for human-AI communication, determining both the content and quality of AI-generated results.

AI validation is the process of determining whether an artificial intelligence system meets its intended purpose and performs correctly across a range of conditions and scenarios.

A Vector DB is a specialized database designed to store and query embeddings, which are numerical representations of unstructured data like text, images, or audio. This allows AI systems to retrieve data based on meaning and relationships rather than exact matches.

A vector store is a specialized database designed to organize and retrieve feature vectors—numerical representations of data like text, images, or audio. These stores are essential in AI and machine learning workflows, enabling high-speed searches, efficient comparisons, and pattern recognition across vast datasets.

AI versioning is the systematic tracking and management of changes to artificial intelligence models, their code, data, and environments throughout their lifecycle. It creates a historical record that enables reproducibility, collaboration, and responsible deployment of AI systems.

Zero-shot prompting refers to the practice of guiding a language model to perform a task through a direct instruction without including any examples of the task in the prompt.

llama.cpp is a fast, hackable, CPU-first framework that lets developers run LLaMA models on laptops, mobile devices, and even Raspberry Pi boards—with no need for PyTorch, CUDA, or the cloud.

vLLM is a purpose-built inference engine that excels at serving large language models (LLMs) at high speed and scale—especially in GPU-rich, high-concurrency environments.