Technical terms in Gen AI.

Are you familiar with the technical terms used in the context of Gen AI? It's important to understand basic concepts in order to utilize the available AI code generators and execute projects with ease. Here are a few terms explained:

- Fine-tuning: A technique in machine learning where an already trained model is further trained on a new dataset for better performance on a specific task.

- Semantic-embedding: A representation of text in a high-dimensional space where distances between points correspond to semantic similarity.

- Cosine similarity: A metric used to measure how similar two vectors are, typically used in the context of semantic embeddings to assess similarity of meanings.

- Vector databases: Specialized databases designed to store and handle vector data, often employed for facilitating fast and efficient similarity searches.

- Domain-Specific Task: A task that is specialized or relevant to a particular area of knowledge or industry, often requiring tailored AI responses.

- Prompt: An input given to the model to generate a specific response or output.

- Prompt Tuning: A method to improve AI models by optimizing prompts so that the model produces better results for specific tasks.

- Hard Prompt: A manually created template used to guide an AI model's predictions.

- Soft Prompt: A series of tokens or embeddings optimized through deep learning to help guide model predictions.

- One-shot prompting: Giving an AI model a single example to learn from before it attempts a similar task.

- Few-shot prompting: Providing an AI model with a small set of examples, such as five or fewer, from which it can learn to generalize and perform tasks.

- Zero-shot prompting: The capability of an AI model to correctly respond to a prompt or question it hasn't explicitly been trained to answer, relying solely on its prior knowledge and training.

- Chain-of-Thought Prompting: A method of guiding a language model through a step-by-step reasoning process to help it solve complex tasks by explicitly detailing the logic needed to reach a conclusion.

Stay tuned for subsequent articles where we'll delve