Prompt Chaining

What is Prompt Chaining?

Prompt chaining is the practice of structuring AI tasks as a sequence of calls, where each call receives the output of the previous one as part of its context. Rather than asking a model to do everything in a single prompt — classify a document, extract its fields, validate the values, and route it to the right system — you break the task into discrete steps and chain them together.

Each step in the chain has a narrow, well-defined job. This makes individual outputs easier to validate, easier to debug, and more reliable. A prompt that tries to do five things at once tends to do all five poorly. Five prompts that each do one thing well produce compounding accuracy.

When to Use Prompt Chaining

Not every task needs a chain. A simple classification or extraction with a clear schema can often run in one prompt. Chains become valuable when:

• The task requires reasoning that builds on intermediate conclusions

• Different steps require different model configurations (deterministic extraction followed by generative summarization)

• You need to validate or branch based on intermediate outputs

• The full task would exceed a single model's context window

• You want to log and audit each step independently

Prompt Chaining in Operations

A practical example: processing an incoming supplier invoice. Step 1 — classify the document type (invoice, credit note, statement). Step 2 — extract header fields (supplier, invoice number, date, total). Step 3 — extract line items as structured JSON. Step 4 — validate line item totals against the header amount. Step 5 — match against open POs in the ERP. Each step is auditable. If step 3 produces a malformed line item, you know exactly where the failure occurred. This is the difference between an AI workflow you can trust in production and one you have to babysit.