Prompt chaining is a technique where you break down a complex task into several smaller prompts that follow each other. The output of the first prompt becomes the input for the second, and so you build step by step towards a final result. Instead of asking for everything at once, you guide the AI through a process.<\/p>\n
Anyone who regularly works with ChatGPT or Claude will recognise the problem: an extended prompt with ten instructions rarely produces a result that is right on all counts. Prompt chaining solves that by giving each step focused attention.<\/p>\n
Prompt chaining starts by identifying the subtasks within your assignment. Suppose you want to write an internal advisory report based on three source documents. Instead of sending one prompt with \u201canalyse these sources, identify key insights, write a summary and formulate an opinion\u201d, break that down into separate steps.<\/p>\n
The first prompt focuses solely on analysing the sources. You ask the AI to list the key points for each document, without interpreting or summarising already.<\/p>\n
The second prompt takes those key points as input and asks to identify patterns and contradictions. What is reflected in all the sources? Where do they contradict each other?<\/p>\n
The third prompt translates that analysis into advice for a specific target audience, for example the management team. Only in this step do you instruct on tone, length and structure.<\/p>\n
The principle is the same with every prompt chain: each step has exactly one goal, and the output of one step feeds the next. This creates a pipeline in which the end result is better than what a single prompt would produce.<\/p>\n
There are different patterns for building such a chain. The most commonly used pattern is the linear chain: each prompt is executed in sequence and builds on the previous result. This works for tasks with a natural step-by-step progression, like the example above.<\/p>\n
Another pattern is the parallel chain. You issue several prompts simultaneously, each focusing on a different aspect of the same task. Then you combine the results in a concluding prompt. This works when you need independent analyses that you want to combine into one conclusion, for example a SWOT analysis where you have strengths, weaknesses, opportunities and threats each worked out separately.<\/p>\n
A third pattern is the self-correction chain. The model first generates a draft, then evaluates that draft against criteria you provide, and adjusts the result based on that evaluation. This pattern is effective for writing tasks where you want to increase quality iteratively without manually reviewing each version yourself.<\/p>\n
Prompt chaining improves output because it concentrates the AI's attention on a step-by-step basis. A language model that processes 10 instructions simultaneously gives more weight to some than others. This leads to output that is good on three and mediocre on seven.<\/p>\n
With short, focused prompts, you avoid that dilution. Each prompt gets the model's full attention, which increases the quality per step.<\/p>\n
A second advantage is error detection. If step three of your chain produces a weak result, you only re-run that step. With one major prompt, you start all over again, without knowing which part caused the problem. You can make targeted adjustments instead of repeating everything.<\/p>\n
The third advantage is that you can improve the quality of your prompt one step at a time. If a particular step consistently produces mediocre output, you adjust only that prompt. This targeted approach is consistent with the principle that a good question to AI<\/a> should be specific and delineated.<\/p>\n Prompt chaining and chain-of-thought prompting are similar, but work fundamentally differently. The difference is in where the reasoning process takes place: spread across multiple prompts, or within a single prompt.<\/p>\n In chain-of-thought prompting, you ask the model to think step by step within one prompt. You add an instruction like \u201creason step by step\u201d and the model goes through the entire thinking process in one response. This works well for logical problems, calculations and analyses that require a clear line of reasoning.<\/p>\n In prompt chaining, you split the task across several separate prompts. Each prompt is its own interaction, and you assess the intermediate result before deploying the next step. This gives more grip on the process, but also takes more time. In practice, prompt chaining proves to have added value mainly in tasks where the output goes to someone else, such as reports, quotations or communications.<\/p>\n The two techniques are not mutually exclusive. You can also use chain-of-thought within a prompt chain at a specific step, for instance when that step requires complex trade-offs. Those who properly understand How ChatGPT generates text<\/a>, sees why that combination works: the model processes each prompt as a standalone task, and chain-of-thought helps with steps that require reasoning within that task.<\/p>\n Prompt chaining becomes concrete using a realistic example. Suppose that, as a communications consultant, you need to write a press release about an organisational change. The management has sent you an internal memo with the details.<\/p>\n Prompt 1: \u201cRead this internal memo and identify the five most important facts that should be in the press release. No interpretation, just facts.\u201d<\/p>\n Prompt 2: \u201cRank these five facts by relevance to an external audience. Which fact belongs in the opening paragraph? Which ones are supportive?\u201d<\/p>\n Prompt 3: \u201cWrite a press release of up to 300 words based on this ranking. Use a businesslike tone. The opening sentence contains the most important fact. Do not make up quotes.\u201d<\/p>\n Prompt 4: \u201cCheck this press release for factual consistency with the original memo. Are there any claims that are not directly reducible? If so, highlight them.\u201d<\/p>\n Each step has one function: extract facts, prioritise, write, verify. The fourth step is a built-in verification that you don't have with a single prompt. You see exactly what the model changes or adds, and you can intervene before the press release goes out the door.<\/p>\n A second real-life example: a project manager who needs to prepare a risk analysis. The first prompt identifies all risks based on the project plan. The second prompt categorises those risks by likelihood and impact. The third prompt formulates a mitigation measure for each risk. The fourth prompt brings everything together in a structured overview that can be used immediately in a steering committee meeting.<\/p>\n The self-correction chain works differently. Suppose you need to write a quotation. Prompt 1 generates a first version based on your briefing. Prompt 2 assesses that version based on criteria you give: \u201cCheck whether the quotation contains a clear problem statement, describes a concrete approach and quotes a substantiated price.\u201d Prompt 3 rewrites the quotation based on that assessment. The model corrects itself, so you need fewer manual rounds of revision.<\/p>\n This kind of chain works in Claude<\/a>, ChatGPT and other language models in the same way. It is not about which model you use, but how you divide up the task. The structure of the chain determines the quality of the result.<\/p>\n Prompt chaining produces the best results if you follow a few rules of thumb when setting up your chain.<\/p>\n Split as soon as a prompt tries two things at once. \u201cAnalyse and summarise\u201d are two operations that each require a different instruction. Make them two prompts, so you can adjust one step at a time if the output is incorrect.<\/p>\n Limit your chain to a maximum of five steps. Longer chains increase the chances of context being lost or small errors piling up. If you need more than five steps, the original task is probably too broadly defined.<\/p>\n Be explicit about how the model should use the previous output. \u201cUse the output from the previous step\u201d is too vague. \u201cTake the five key points from the previous step and rank them by urgency\u201d gives the model a clear mandate. In the ChatGPT course<\/a> from LearnLLM you will learn to apply these kinds of techniques structurally in your own work.<\/p>\n Build in a review step after critical intermediate steps. A question like \u201cwere facts omitted or added compared to the original input?\u201d is enough to catch errors early, before they propagate through the rest of the chain.<\/p>\n Test each step individually first before running the full chain. A prompt that works well in isolation but gives an unexpected result in combination with the previous output is easier to debug if you have tested it separately first.<\/p>\n Prompt chaining is not the right approach for every task. The technique makes sense for tasks that require multiple operations or require different types of output. Think of tasks that combine research, analysis, creation and verification into one final product.<\/p>\n For simple tasks, prompt chaining is unnecessary. A AI chatbot<\/a> who asks you to summarise a meeting note does not need a chain of four prompts. One well-worded prompt with clear instructions on length, tone and structure will suffice.<\/p>\n A good rule of thumb: if you can describe the task in one sentence without using the word \u201cand\u201d, a single prompt is probably sufficient. \u201cSummarise this note\u201d is one task. \u201cAnalyse this report, identify risks and write a recommendation for management\u201d is three tasks that benefit from prompt chaining.<\/p>\n Professionals just starting to link prompts often make the mistake of building chains that are too long. An eight-step chain sounds thorough, but in practice, context is lost after step five. Start with chains of two or three steps and expand as you notice that results improve.<\/p>\n Another common mistake is not conveying enough context between steps. Don't assume that the model remembers everything. With each new prompt, provide the relevant context it needs to perform the task.<\/p>\n Prompt chaining forces you to think about the structure of your assignment beforehand. Which steps are needed? In what order? Where is the risk of error? That preparation makes the difference between ad hoc prompting and a repeatable process.<\/p>\n A prompt chain that works well for writing an advisory report can be used again next time. Document your chains to build a library of working workflows. Generative AI<\/a> yields the best results when you structure the process, not when you improvise every time.<\/p>\n Such a library need not be complicated. A document with the steps, prompts and notes on what worked and what didn't work for each task type is enough. After a few weeks, you have a collection of chains that you can deploy immediately without having to rethink the structure each time.<\/p>\nPrompt chaining versus chain-of-thought prompting<\/h2>\n
A practical example of prompt chaining<\/h2>\n
Tips for setting up a prompt chain<\/h2>\n
When prompt chaining does and does not make sense<\/h2>\n
From loose prompts to an established way of working<\/h2>\n