Custom Instructions in ChatGPT, also known as Tailored Preferences are the best way to control ChatGPT’s output easily. Example 1: Like, if you are drained of typing “Give a brief response” or “Please respond in length” like me? Try following this instruction. If I add * at the end of my question, please provide a concise, to-the-point response. If I add **, provide a full and comprehensive response. If I do not provide any symbols, please provide a standard response. Example 2: We can control the model’s temperature using APIs, but there is no way to do it through the UI. You can use the following instruction to have GPT follow temperature. If I specify a temperature range of 0 to 1 at the conclusion of the question, please react accordingly: Temperature 0 indicates highly deterministic responses, thus you should always run a web search before responding. Temperature 1 gives for greater creativity and freedom in reaction.
1. Generative AI A subset of artificial intelligence focused on creating new content, such as text, images, audio, video, or code. It relies on models trained on vast datasets to identify patterns and generate similar but unique outputs. 2. Large Language Model (LLM) An AI model trained to process and generate human-like text. Examples include GPT (Generative Pre-trained Transformer), BERT, and LaMDA, all of which leverage deep learning architectures, specifically transformers. 3. Transformer A neural network architecture known for its ability to process sequential data, like text. Transformers use self-attention mechanisms, enabling the model to learn relationships between words and capture long-range dependencies efficiently. 4. Retrieval-Augmented Generation (RAG) A hybrid approach combining retrieval-based and generative AI techniques. RAG models retrieve relevant information from external sources (e.g., databases or documents) and then use this information to generate contex...