You understand the idea of prompt chaining and you want to build one. This is the procedure. It moves from a blank page to a tested chain in a fixed sequence of steps, with a clear action at each stage. Follow it in order and you will have a working pipeline by the end of the day.
The process below is deliberately concrete. Rather than discussing chaining in the abstract, it tells you what to write, what to check, and what to do next at each point. The example task throughout is turning a raw customer review into a structured insight a product team can act on, but the same steps apply to any chainable task.
Treat this as a recipe. The first time through, follow it exactly. Once you have built one chain this way, you will adapt the procedure to your own tasks naturally.
Step One: Write Down the Whole Task in One Sentence
Before splitting anything, state the end goal plainly: "Turn a customer review into a structured record with sentiment, the specific complaint, and a suggested action." A clear destination keeps you from over-engineering. If you cannot state the goal in one sentence, you do not understand the task well enough to chain it yet.
Step Two: List the Natural Sub-Tasks
Break the sentence into the jobs that have to happen in order:
- Extract the concrete claims from the review.
- Classify the overall sentiment.
- Map the main complaint to a known category.
- Suggest an action for the product team.
These become your candidate links. You will likely merge some of them in the next step.
Step Three: Decide Where to Draw Link Boundaries
Not every sub-task deserves its own link. Merge steps that the model can reliably do together and split steps where you need to inspect the result. For this task, extraction and sentiment can be one link, while categorization and action stay separate so you can validate the category before generating advice. Our A Framework for Prompt Chaining gives a repeatable way to make these cuts.
Step Four: Define the Output Shape for Each Link
This is the step beginners skip and regret. Decide exactly what each link returns before you write the prompt.
Specify Structure Explicitly
For the first link, the output might be a JSON object with claims as an array and sentiment as one of three values. Writing the shape down first forces clarity and gives you something to validate against.
- State the format: JSON, a numbered list, a single paragraph.
- Name every field and its allowed values.
- Decide what an empty or uncertain result looks like.
Step Five: Write Each Prompt Against Its Input Contract
Now write the actual prompts, one per link. Each prompt should reference only the data the previous link produced, never the original source unless that link genuinely needs it. The categorization link reads the extracted complaint, not the full review. Keeping inputs minimal keeps each link focused and cheap.
Step Six: Run the Chain Manually First
Before automating anything, run the chain by hand on three or four real reviews. Paste each link's output into the next. This is the fastest way to find a broken handoff. If link two chokes on link one's format, you fix it now, on paper, instead of in code. The Prompt Chaining Checklist for 2026 is a useful companion for this manual pass.
Step Seven: Add Validation Between Links
Once the chain works by hand, add a check between each link. Validation does not have to be code at first, but the logic must exist.
Validate Before Passing Forward
After link one returns, confirm the JSON parses and the sentiment value is one of the allowed three. If it is not, stop or retry rather than feeding bad data downstream. Catching a malformed result here prevents a confusing failure three links later.
Step Eight: Test on a Larger Sample and Measure
Run the finished chain on ten to twenty inputs and record where it fails. Look at which link introduced each error. A chain that is 90 percent right overall might have one weak link dragging it down, and now you know exactly which prompt to improve. For more failure patterns to watch for, see 7 Common Mistakes with Prompt Chaining (and How to Avoid Them).
How to Read the Failure Log
When you record failures by link, patterns emerge quickly. If link two fails on the same kind of input every time, the prompt has a blind spot you can fix with a clearer instruction or an example. If failures are scattered randomly across links, the problem is more likely a loose contract somewhere upstream feeding inconsistent data forward. Reading the log is how you turn a vague sense that the chain is unreliable into a specific, fixable defect.
A Concrete Walkthrough of the Review Chain
To anchor the eight steps, here is the customer-review example run all the way through, so you can see each step produce its artifact.
From Goal to Links
The one-sentence goal is to turn a review into a structured record with sentiment, complaint, and action. The sub-task list is extract claims, classify sentiment, categorize the complaint, and suggest an action. Cutting boundaries, extraction and sentiment merge into link one because the model reliably does them together, categorization becomes link two so its result can be validated, and action generation becomes link three.
The Contracts in Practice
- Link one returns a JSON object with a
claimsarray and asentimentfield limited to positive, neutral, or negative. - Link two returns a single
categoryvalue drawn from a fixed list, plus a confidence flag. - Link three returns one suggested action as a short string.
With these contracts written down, the validation step is obvious: after link one, confirm the JSON parses and the sentiment is one of three values; after link two, confirm the category is on the allowed list. Anything else stops the chain. This is exactly the discipline the Prompt Chaining: Best Practices That Actually Work guide treats as non-negotiable.
What the Walkthrough Teaches
Notice how much of the work happened before any prompt was written. By the time you write the actual prompts in step five, the hard decisions, how many links, what each returns, are already made. That front-loading is what makes the chain reliable, and it is the habit that separates a durable pipeline from a prototype that breaks on the first messy input.
Frequently Asked Questions
Should I write code before testing the chain manually?
No. Run the chain by hand on a few real inputs first. Manual testing surfaces broken handoffs and format mismatches faster than debugging code, and it costs nothing to fix the prompts at that stage.
How do I decide where one link ends and the next begins?
Merge sub-tasks the model handles reliably together, and split where you need to inspect or validate an intermediate result. Draw a boundary anywhere you would want to check the output before continuing.
What should each link's prompt actually contain?
A clear instruction, the minimal input it needs from the prior link, and an explicit description of the output shape you expect back. Keep the input small so the link stays focused on its one job.
How many test inputs do I need before trusting the chain?
Start with three or four for the manual pass, then expand to ten or twenty for measurement. The larger sample reveals which link is weakest so you know where to invest your improvement effort.
What do I do when a link returns malformed output?
Validate between links and stop or retry rather than passing the bad data forward. A malformed result that slips downstream causes a confusing failure later, while catching it at the source makes the fix obvious.
Key Takeaways
- State the full task in one sentence before splitting it, so you build only the links you need.
- List natural sub-tasks, then merge and split them into links based on reliability and where you need to inspect results.
- Define the exact output shape of each link before writing any prompt.
- Run the chain manually on real inputs first to catch broken handoffs cheaply.
- Add validation between links so malformed output never propagates downstream.
- Test on a larger sample, record failures by link, and improve the weakest prompt.