Generative AI: Use cases, applications, solutions and implementation

Generative AI is a technology used to generate new and original content. It leverages advanced algorithms and neural networks to autonomously produce outputs that mimic human creativity and decision-making.

Synthetic data refers to artificially generated data that replicates the statistical properties and characteristics of real data without containing any identifiable or sensitive information.

LangChain is a framework that provides a set of tools, components, and interfaces for developing LLM-powered applications.

Data annotation is adding labels or tags to a training dataset to provide context and meaning to the data.

Parameter-efficient Fine-tuning (PEFT) is a technique used in Natural Language Processing (NLP) to improve the performance of pre-trained language models on specific downstream tasks.

AI has been making significant advancements in the hospitality industry, reforming various aspects of guest experiences, operational efficiency, and overall management.

AI’s capacity to learn from vast datasets can significantly enhance the precision and efficiency of production cycles, diminishing the need for manual intervention in the manufacturing sector.

Language models are the backbone of natural language processing (NLP) and have changed how we interact with language and technology.

Auto-GPT is an autonomous tool that allows large language models (LLMs) to operate autonomously, enabling them to think, plan and execute actions without constant human intervention.

EDA allows for exploring and examining data for important insights before the actual data analysis process begins. Check out this article to explore the importance, process and techniques of EDA.

By understanding the architecture of generative AI, enterprises can make informed decisions about which models and techniques to use for different use cases.

Reinforcement learning from human feedback (RLHF) is a machine learning approach that leverages a combination of human feedback and reinforcement learning to train AI models.