Rectified Flow is a novel method in generative modeling that simplifies and speeds up the generation of complex data, such as images or 3D shapes. Unlike traditional diffusion models that gradually denoise a sample over many steps, rectified flow directly...

RAGAS (Retrieval-Augmented Generation Assessment System) is an evaluation framework designed to measure the performance of retrieval-augmented generation (RAG) systems. RAGAS focuses on how effectively a system retrieves relevant information and generates coherent, factual responses based on that information, addressing both...

RAG (Retrieval Augmented Generation) is a technique that enhances generative AI models—such as large language models—by integrating external, domain-specific knowledge sources into the generation process. Rather than relying solely on patterns learned from pre-training, RAG dynamically fetches relevant information from...

PyTorch is an open-source deep learning framework developed by Meta AI. It provides a flexible and intuitive platform for building and training machine learning models. Unlike some static frameworks, PyTorch uses a dynamic computation graph, which makes debugging and model...

Prompt Injection is a security vulnerability in AI systems, particularly large language models (LLMs). It occurs when an attacker manipulates model behavior by inserting malicious instructions into user input. As a result, the model may bypass intended restrictions, leak information,...

Prompt Engineering is the process of crafting effective inputs to guide the behavior of large language models (LLMs) and other AI systems. By carefully designing prompts, users can influence the quality, relevance, style, and accuracy of AI-generated outputs. Prompt engineering...

Pre-training is the process of training a machine learning model on a large, general-purpose dataset before adapting it to a specific task. By learning broad patterns, structures, and representations from unlabeled or widely available data, a model develops a rich...

Post-training refers to the stage that follows the initial model training process, focusing on refining, optimizing, and preparing the model for deployment in real-world scenarios. After a model has been trained—often through pre-training and fine-tuning—it may still benefit from additional...

In the context of natural language processing (NLP) and machine learning, perplexity is a metric used to evaluate the performance of language models. It measures how well a model predicts a sequence of words, with lower perplexity indicating better performance....

Parameter refers to the internal variables of a machine learning model that are learned from training data. These values define how input features are transformed into outputs, and they are crucial for determining the model’s behavior and accuracy. Parameters are...