Prompt Chaining is a technique where you connect multiple AI prompts in sequence, so the output from one step becomes the input for the next. Think of it like an assembly line. Instead of asking an AI to complete a complicated task all at once, you break it into smaller steps. Each step handles one specific piece of the work, then passes its results forward.

For example, imagine processing customer support tickets. A single prompt asking "handle this ticket" might produce inconsistent results. With prompt chaining, you'd break it into steps: first, classify the ticket type (billing, technical, returns).

Then, based on that classification, route it to the appropriate next step. If it's a return, pull up the order details. Then check the return policy. Finally, draft a response using all that context. Each step does one thing well, and the outputs stack together into a reliable workflow.

Prompt chaining gives you more control and transparency. You can see what's happening at each stage, adjust individual steps without breaking the whole process, and insert human approval checkpoints wherever you need them. For businesses automating operations, this structured approach turns unreliable single-prompt attempts into dependable, auditable processes.

Frequently Asked Questions

How is prompt chaining different from just writing one really good prompt?

A single prompt, no matter how detailed, asks the AI to handle everything at once. It's like asking someone to "plan a wedding" versus giving them a checklist: book venue, hire caterer, send invitations, etc.

Prompt chaining breaks the work into discrete steps. This makes each step simpler for the AI, easier for you to debug when something goes wrong, and more consistent in its results. You're trading one complex instruction for a series of clear, simple ones that build on each other.

When should I use prompt chaining instead of a simple prompt?

Use prompt chaining when your task has multiple distinct stages that depend on each other. For instance, if you're processing invoices, you might need to: extract data, validate it against a purchase order, check approval limits, and then route for payment.

Each of those steps benefits from focused instructions and can fail independently. If your task is straightforward, a single prompt is fine. But for multi-step processes where each stage needs different logic or data, chaining gives you the structure and reliability you need.

Can I mix prompt chaining with retrieving data from my systems?

Yes, that's actually one of the most powerful uses. At each step in your chain, you can pull data from databases, ERPs, or other systems to inform the next prompt.

For example, step one might extract a vendor name from an email. Step two uses that name to query your ERP for existing contracts. Step three then uses those contract terms to validate pricing in the original email.

This combination of structured reasoning and live data access is how you build AI agents that work with your actual business systems.

How do I know if my prompt chain is too long or too short?

A good rule: each step should do one clear thing. If you find yourself writing prompts like "analyze this, but also check that, and then compare these," you probably need to split it.

On the flip side, if you have ten steps that could easily be three, you're over-engineering. Start with the major decision points and data transformations in your process. Those become your steps.

You can always refine from there based on where you see inconsistency or errors.

What happens when one step in the chain fails or produces bad output?

This is where prompt chaining shines. Because each step is isolated, you can catch failures early. You might add validation rules after each step. For instance, if step two is supposed to return a dollar amount but returns "N/A," you can stop the chain and flag it for review rather than letting garbage data propagate through.

You can also build in confidence scoring, where the AI at each step indicates how certain it is, and low-confidence outputs get routed to a human. This gives you control over quality at every stage.

Does prompt chaining make things slower since you're running multiple prompts?

Yes, chaining takes longer than a single prompt because you're making multiple API calls. However, the tradeoff is usually worth it. The increased reliability and accuracy often outweigh the extra seconds. Plus, you can optimize by running independent steps in parallel when they don't depend on each other.

For example, if you need to look up customer data and product data separately before combining them in step three, you can fetch both simultaneously. The key is to structure your chain so the critical path stays fast.

How does this relate to AI agents? Are they the same thing?

Prompt chaining is a technique AI agents use, but agents are broader. An AI agent can decide which prompts to chain together based on the task it's given, loop back to earlier steps if needed, and interact with external systems.

Prompt chaining is the underlying structure. Think of chaining as the recipe, and the agent as the chef who knows when to follow which recipe and how to adapt if something unexpected happens. Agents use chaining as one tool in their toolkit to reliably execute complex workflows.

What are the risks of using prompt chaining in business processes?

The main risks are complexity and dependency. If you chain too many steps together, debugging becomes harder. If one step depends on perfect output from the previous step, small errors can cascade. You also need to manage context carefully.

Too much data passed between steps can confuse the AI; too little means it lacks information to make good decisions. Testing is critical. You need to validate that each step handles edge cases and that the chain as a whole produces the right outcomes across different scenarios.

Zamp addresses this through structured workflows with built-in quality controls. Our agents use prompt chaining but include automatic validation at each step, activity logs that show exactly what happened where, and "Needs Attention" flags for outputs that don't meet confidence thresholds.

You can configure approval checkpoints at any stage, so nothing goes through automatically until you're confident the chain is working. This gives you the benefits of structured automation with the safety of human oversight where it matters.

Can I update or improve individual steps in a chain without breaking everything?

Yes, and that's one of the major advantages. Because each step is self-contained, you can iterate on one without touching the others.

For example, if your invoice extraction step isn't catching certain fields, you can improve just that prompt while leaving your validation and routing steps unchanged. This modularity makes continuous improvement much easier than trying to optimize one giant, monolithic prompt.

You can A/B test variations of individual steps to see what works best, all while the rest of the chain keeps running.