// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Prompt Chaining
This involves breaking down a complex task into smaller steps, where the output of one AI prompt becomes the input for the next prompt in a sequence.
TECHNICAL DEFINITION
Prompt chaining is a technique where the output of one large language model (LLM) prompt serves as the input for a subsequent prompt, enabling the decomposition of complex tasks into a series of manageable, interconnected sub-tasks to achieve a final goal.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt contexts supplied to the GenAI model, such as metadata, API tools, and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Sequential prompting
- Multi-stage prompting
- Iterative prompting
- Workflow prompting
USAGE NOTE
Prompt chaining is effective for complex workflows that require multiple reasoning steps or data transformations.
DEVELOPERS
Organizations developing technology related to Prompt Chaining.
LangChain is a framework designed for developing applications powered by language models. It provides tools and abstractions, including the concept of 'chains,' to link multiple components together, effectively enabling prompt chaining for complex workflows.
LlamaIndex focuses on connecting large language models with external data. Its query engines often involve multi-step processes where the output of one LLM interaction (prompt) is used to inform the next, a clear application of prompt chaining.
As a leading developer of large language models like GPT, OpenAI's API and research heavily influence and enable prompt engineering techniques, including prompt chaining, for building sophisticated AI applications.
Hugging Face provides open-source libraries (like Transformers) and a platform that serves as a hub for AI models and tools. Developers frequently use their ecosystem to implement complex LLM applications that rely on prompt chaining for task decomposition and execution.
Microsoft offers enterprise-grade services for deploying and managing OpenAI models through Azure. Their tooling and guidance for building robust AI solutions often involve advanced prompt engineering and orchestration, including prompt chaining, for complex business logic.
Google's Vertex AI platform provides tools for building, deploying, and scaling machine learning models, including their own large language models. Developers use Vertex AI to create sophisticated AI agents and workflows that incorporate prompt chaining for multi-turn conversations and complex reasoning.
Vellum AI offers a platform for prompt engineering, testing, and deployment. It provides tools to manage and optimize prompts, including the orchestration of complex prompt sequences and chaining for improved model performance and reliability.
As a developer of advanced AI models like Claude, Anthropic's research and practical applications frequently involve sophisticated prompt engineering strategies, where prompt chaining is a key technique to guide the model through multi-step tasks and reasoning.