Prompt Chaining
Prompt chaining is a technique where the output of one language model call becomes the input for the next, creating a pipeline of AI reasoning steps. It enables complex workflows that exceed what a single prompt can accomplish.
Understanding Prompt Chaining
Prompt chaining is a technique where the output of one language model call is used as input for a subsequent call, creating a sequence of connected prompts that collectively accomplish a complex task. Rather than attempting to solve a multifaceted problem in a single prompt, chaining breaks it into manageable steps—for example, first extracting key facts from a document, then synthesizing those facts into a summary, and finally translating the summary into another language. This approach improves reliability and interpretability by making each step auditable. Prompt chaining is a foundational technique in agentic AI systems and is closely related to tool use, where intermediate steps may involve calling external APIs or databases. It works well with few-shot prompting at each stage and is widely used in production applications built on large language models like Claude to handle workflows that require planning, reasoning, and multi-step execution.
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.