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.
Understanding Few-Shot Prompting
Few-shot prompting is a technique where a small number of input-output examples are included directly in the prompt to guide a large language model toward the desired behavior without any parameter updates. For instance, providing three examples of sentiment classification before asking the model to classify a new review helps it understand the expected format and task. This approach leverages the in-context learning capabilities of transformer-based generative models and sits between zero-shot prompting and traditional fine-tuning in terms of effort and effectiveness. Few-shot prompting is widely used in applications like text classification, translation, and data extraction where labeled training data is scarce. It forms a foundational technique in prompt engineering and is often combined with prompt chaining to handle more complex, multi-step workflows in agentic AI systems.
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.
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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.
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.
Gemini
Gemini is Google's family of multimodal AI models capable of processing text, images, audio, and video. It represents Google's most advanced AI system and competes with models like GPT-4 and Claude.