Colorful cables bundled and organized with white ties representing connected and sequenced data pathways — Photo by Paris Bilal on Unsplash

What Is Prompt Chaining?

Q: What is prompt chaining?

Prompt chaining is a technique where the output of one language model call is used as the input for the next, creating a sequence of connected LLM calls that together complete a complex task. Each step in the chain has a focused, specific purpose, and validation between steps ensures quality before proceeding.

Q: When should I use prompt chaining instead of a single complex prompt?

Use prompt chaining when a task has multiple distinct stages that benefit from separate focus — for example, first extracting key information, then analyzing it, then formatting the output. A single long prompt tries to do all of this in one step, which increases the chance of the model mixing concerns. Chaining enforces separation of responsibilities.

Q: How is prompt chaining different from an agent loop?

Prompt chaining follows a fixed, predefined sequence of steps determined by the developer. An agent loop is dynamic — the model itself decides what to do next based on observations. Prompt chaining offers more control and predictability. The agent loop offers more adaptability for tasks with uncertain paths.

Quick Definition#

Prompt chaining is the technique of linking multiple language model calls in sequence, where the output of one call becomes the input for the next. Instead of trying to solve a complex task in a single prompt, chaining breaks the work into focused stages, each responsible for one clear sub-problem. The result of each stage is passed forward, building toward the final output step by step.

Prompt chaining is a foundational pattern in AI systems and is often the right starting point before adopting more dynamic architectures like the Agent Loop. For context on where chaining fits in the broader agent design landscape, see What Are AI Agents?. Browse the full AI Agents Glossary for all prompting and workflow terms.

Why Prompt Chaining Matters#

Long, complex prompts create a challenge: the more a single call tries to accomplish, the more likely the model is to blend concerns, lose track of constraints, or produce inconsistent outputs. A call that asks the model to extract facts, analyze sentiment, check policy compliance, and produce a formatted report in one pass will often produce weaker results than a chain that handles each step separately.

Prompt chaining addresses this by enforcing focus. Each call does one thing well, and its output is a clean handoff to the next stage. This produces more reliable, auditable, and maintainable workflows.

For platform-level comparison of tools that support chaining patterns, see Best AI Agent Platforms in 2026.

Anatomy of a Prompt Chain#

A typical prompt chain has three components:

1. Input Stage#

The chain begins with a raw input — a user message, a document, a structured data record, or a trigger event. This input is passed to the first prompt in the chain.

2. Processing Stages#

Each intermediate stage receives the output of the previous stage plus any additional context, performs a focused operation, and produces a structured output for the next stage. Examples of processing stages:

Extraction stage: pull specific fields or facts from unstructured text
Classification stage: categorize the extracted information
Analysis stage: reason about the classified information in context
Generation stage: produce a draft output based on the analysis

3. Output Stage#

The final stage formats and delivers the result. This might be a structured JSON object, a formatted document, a user-facing response, or a trigger for a downstream action.

When to Use Prompt Chaining#

Good fit:

The task has clearly separable stages with clean handoff points
You need to validate quality at each step before proceeding
Different stages benefit from different prompting strategies or models
You want interpretable, auditable intermediate outputs
The workflow path is fixed and well-understood

Poor fit:

The task requires dynamic decision-making about which steps to take
The path forward depends on the results of previous steps in unpredictable ways
You need the model to select from a range of available tools at runtime

When the workflow path is dynamic, a full Agent Loop with Agent Planning is more appropriate than a fixed chain.

Validation Between Chain Steps#

Validation between steps is one of the most important practices in prompt chaining and one of the most commonly skipped.

Without validation, errors propagate silently through the chain. If step one produces a malformed or incomplete output, step two will build on that faulty foundation, and by the time the error becomes visible, the source is buried several steps back.

Effective validation approaches:

Schema validation: After each step, check that the output matches the expected JSON schema or structure before passing it to the next step.

Confidence scoring: Include a self-evaluation step where the model rates its own output quality and triggers a retry or escalation if confidence is low.

Assertion checks: Run rule-based checks on critical fields — for example, verify that a classification output contains only valid category values.

Human review gates: For high-stakes chains, insert a human review checkpoint between stages where an incorrect output could cause significant downstream damage.

Practical Example: Content Analysis Chain#

Suppose you need to process customer feedback at scale. A prompt chain might look like:

Extraction call: "Extract the product name, sentiment, and core complaint from this feedback."
Validation: Check that all three fields are present and non-empty.
Classification call: "Given this complaint, classify it as billing, product quality, shipping, or other."
Validation: Verify the classification is one of the allowed values.
Routing call: "Based on this classification, write a triage summary for the support team."
Final output: Structured summary ready for CRM ingestion.

Each step is small, focused, and validated. Failures are caught early and attributed to the correct stage.

For implementation examples, see Build an AI Agent with LangChain.

Prompt Chaining vs. Agent Loop#

The most important distinction for builders:

| Dimension | Prompt Chaining | Agent Loop | |-----------|-----------------|------------| | Step sequence | Fixed, predefined | Dynamic, decided at runtime | | Control | Developer-controlled | Model-controlled | | Adaptability | Low — follows fixed path | High — responds to observations | | Predictability | High | Lower | | Best for | Well-structured, known workflows | Open-ended, variable tasks |

In practice, many systems use both: a fixed chain handles structured, high-frequency workflows, while an agent loop handles edge cases or tasks that require dynamic decision-making.

Integration with Multi-Agent Systems#

In Multi-Agent Systems, prompt chaining often appears at the level of individual agent workflows. An orchestrator agent might trigger a specialized agent that internally uses a prompt chain to complete its assigned task. The chain output is then returned to the orchestrator, which continues the higher-level coordination loop.

Implementation Checklist#

Define clear input and output contracts for each chain step.
Add schema validation between every step.
Log inputs and outputs at each stage for debugging.
Test each step in isolation before testing the full chain.
Add retry logic for transient failures at the step level.
Insert human review gates for high-stakes intermediate outputs.
Monitor chain performance metrics: step success rate, latency per step, and end-to-end completion rate.

Frequently Asked Questions#