System Prompt
A system prompt is a hidden instruction given to a language model that defines its behavior, persona, and constraints for a conversation. System prompts shape how AI assistants respond without being visible to end users.
Understanding System Prompt
A system prompt is an initial set of instructions provided to a large language model that defines its behavior, personality, constraints, and response format before any user interaction begins. It acts as a persistent context that shapes every subsequent response, enabling developers to create specialized AI assistants, enforce safety guidelines, and maintain consistent output styles. System prompts are fundamental to building AI applications on top of foundation models, allowing the same base model to function as a customer support agent, coding assistant, or creative writing partner. Effective system prompt engineering requires careful wording to balance helpfulness with safety guardrails. The system prompt works alongside techniques like few-shot prompting and chain of thought to guide model behavior without modifying the underlying model weights through fine-tuning.
Category
Generative AI
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Chain of Thought
Chain of thought is a prompting technique that encourages large language models to break down complex reasoning into intermediate steps. This approach significantly improves performance on math, logic, and multi-step reasoning tasks.
ChatGPT
ChatGPT is an AI chatbot developed by OpenAI that uses large language models to generate human-like conversational responses. It became one of the fastest-growing consumer applications in history after its launch in November 2022.
Claude
Claude is an AI assistant developed by Anthropic, designed to be helpful, harmless, and honest. It is built using Constitutional AI techniques and competes with models like GPT-4 and Gemini.
Diffusion Model
A diffusion model is a generative AI model that creates data by learning to reverse a gradual noise-adding process. Diffusion models power state-of-the-art image generation systems like Stable Diffusion and DALL-E.
Discriminator
A discriminator is the component of a GAN that learns to distinguish between real and generated data. It provides feedback to the generator, creating an adversarial training dynamic that improves output quality.
Few-Shot Prompting
Few-shot prompting provides a language model with a small number of input-output examples in the prompt to demonstrate the desired task format. This technique helps models understand task requirements without any fine-tuning.
Foundation Model
A foundation model is a large AI model trained on broad data that can be adapted to a wide range of downstream tasks. GPT-4, Claude, Gemini, and DALL-E are examples of foundation models that serve as bases for specialized applications.
GAN
A GAN (Generative Adversarial Network) is a generative model consisting of two competing neural networks — a generator and a discriminator. GANs produce realistic synthetic data by training these networks in an adversarial game.