Скачать или смотреть Prompt Chaining: The Foundational Pattern for Building Reliable Multi-Step AI Agents

Prompt chaining, also known as the Pipeline pattern, is a foundational paradigm for leveraging large language models (LLMs) to tackle intricate, complex tasks.
What Prompt Chaining Is: Instead of relying on a single, monolithic prompt that often causes the LLM to struggle with constraints and overlook instructions, prompt chaining utilizes a divide-and-conquer strategy. The technique breaks down a daunting original problem into a sequence of smaller, more manageable sub-problems. This sequential processing introduces modularity and clarity, making the overall process more robust and easier to debug.
How It Works: The core mechanism establishes a dependency chain where the output generated from one prompt is strategically fed as input into the subsequent prompt.
This sequential decomposition significantly improves the reliability and control of the workflow. By reducing the cognitive load on the model, it lowers the chance of errors like instruction neglect, contextual drift, or hallucination.
Key Architectural Requirements:
• Structured Output: To ensure the integrity and reliability of data passed between steps, it is crucial to specify structured output formats, such as JSON or XML. This practice minimizes errors that arise from interpreting ambiguous natural language.
• Role Assignment: The process can be enhanced by assigning a distinct role (e.g., "Market Analyst," "Trade Analyst") to the model at every stage, helping to ensure an accurate response for each specific task.
• Tool Integration: Prompt chaining allows for the integration of external knowledge and tools between steps, enabling the LLM to perform actions or access external data, such as delegating arithmetic calculations to an external calculator tool.
Application in Agentic Systems: This pattern is foundational for building sophisticated AI agents capable of multi-step reasoning, planning, and executing complex workflows. Frameworks like LangChain/LangGraph and Google ADK provide robust tools to define, manage, and execute these multi-step sequences. You should use this pattern when a task involves multiple distinct processing stages or requires interaction with external tools between steps.

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