What it is
Prompt chaining is a design pattern where a complex task is broken into a series of smaller, focused prompts, each building on the output of the previous one. Rather than asking an LLM to do everything in one shot, chaining creates a pipeline of reasoning steps that are easier to test, debug, and control. It is a foundational technique inside agentic workflows and multi-step automations.
Why it matters
Single large prompts are brittle and hard to maintain — prompt chaining makes AI logic modular and testable. It also reduces hallucination risk by narrowing the scope of each individual inference call. Mid-market teams benefit because chains can be versioned and updated without rebuilding entire agent flows.
Key components
- step decomposition
- output parsing
- conditional branching
- context forwarding
- chain orchestrator
How it connects
Salesforce Flow Builder and Prompt Builder both support chained prompt patterns, and the Atlas Reasoning Engine uses chaining internally when decomposing complex agent goals into subtasks.
Good to know
Log the input and output of every chain link during testing — failures almost always occur at a specific link, not across the whole chain.
Related terms
Prompt Engineering
The practice of crafting precise instructions to guide an AI model's behavior, capabilities, and limitations.
Reasoning Model
A class of large language model trained to spend hidden internal "thinking" tokens before producing a user-facing answer — often dramatically improving performance on math, code, science, and complex multi-step problems compared to non-reasoning models of similar size.
Atlas Reasoning Engine
The AI brain inside Agentforce that plans multi-step actions, evaluates options, and decides what to do next — all grounded in your Salesforce data.
Prompt Builder
Salesforce's tool for creating reusable, templatized prompts that pull in live CRM data. Build once, use across flows, agents, and record pages.