Prompt Engineering
Prompt engineering is the practice of designing and optimizing input prompts to get the best possible responses from AI language models. Techniques include few-shot examples, chain of thought, and structured formatting.
Understanding Prompt Engineering
Prompt engineering is the practice of crafting and refining input prompts to optimize the outputs of large language models for specific tasks, effectively programming AI behavior through natural language rather than code. Techniques include zero-shot prompting, where the model receives only task instructions; few-shot prompting, which provides example inputs and outputs; chain-of-thought prompting, which encourages step-by-step reasoning; and role-based prompting, where the model adopts a specific persona. Prompt engineering has emerged as a critical skill as organizations integrate generative AI into workflows spanning customer support, content creation, data analysis, and software development. The field bridges the gap between model capabilities and practical utility, since the same model can produce vastly different quality outputs depending on how it is prompted. Effective prompt engineering reduces the need for costly fine-tuning and enables rapid iteration on AI-powered applications.
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.