Prompt Chaining

Prompt Chaining is an advanced prompt engineering technique where the output of one prompt becomes the input for another, creating a linked sequence of interactions with an AI model. This method is essential when solving complex problems that require multiple reasoning steps, transformations, or refinements. Instead of asking the AI to handle everything in a single prompt—which can cause information overload or errors—Prompt Chaining breaks the process into smaller, more manageable stages.
You would use Prompt Chaining when a task involves multi-step reasoning, progressive data refinement, summarizing then expanding, or when generating structured workflows. For example, in data analysis, you might first extract key points from raw text and then feed those points into a second prompt for insights. In creative work, you might generate a story outline first, then expand it into full prose.
In this tutorial, you will learn how Prompt Chaining works, how to build effective chains, and how to avoid common mistakes. You will see practical, tested prompt examples that you can copy, adapt, and use in real professional scenarios. These include content creation workflows, research assistance, summarization pipelines, and report generation. By the end, you’ll have a toolkit of chaining techniques ready to integrate into your AI projects, allowing you to guide the model with precision and produce higher-quality outputs in a predictable way.

The Basic Example demonstrates a two-step Prompt Chaining workflow. In the first prompt, the AI is given a clear instruction to summarize the text into exactly five bullet points. This constraint is crucial—it reduces ambiguity, ensures the output is structured, and makes it easier for the next step to process. Asking for "key facts only" prevents opinionated or irrelevant content from being included.
In the second prompt, the AI is tasked with transforming these bullet points into a 150-word executive summary. Here, the specificity of "suitable for a business report" provides contextual guidance for tone, formality, and focus. The defined word count encourages conciseness while ensuring enough detail is preserved.
This chain works because the first stage reduces complexity and standardizes the data, and the second stage builds on that structured base. You can easily modify it—for instance, ask for a 50-word summary for a press release, or an expanded 500-word briefing. You could also add a third stage to generate an infographic outline from the executive summary. Prompt Chaining like this can be adapted for research reports, meeting notes, content repurposing, and educational material generation.

Best practices and common mistakes in Prompt Chaining center on clarity, structure, and control.
Best practices:

1. Keep each step focused on a single task—don’t overload a prompt with multiple unrelated goals.
2. Define the format of outputs precisely (bullet points, tables, word count) to make the next step more reliable.
3. Include necessary context in each prompt so the AI doesn’t lose critical information between steps.

4. Test each link in the chain independently before combining them to ensure stability.
   Common mistakes:

5. Chaining too many prompts without a clear data structure—this compounds errors from earlier steps.

6. Being vague about formatting, causing the next prompt to misinterpret the output.
7. Over-relying on the AI to “remember” details from earlier steps without explicitly including them.
8. Failing to review intermediate outputs, leading to poor final results.
   Troubleshooting tips include re-running a step with refined instructions if the output is noisy, or breaking a step into two smaller ones if it’s too complex. Iterating involves gradually improving prompts based on observed weaknesses, and keeping examples consistent for better model understanding.

Advanced Prompt Chaining opens the door to highly specialized AI workflows. For example, in legal tech, you might chain prompts to extract clauses from contracts, analyze risks, and generate negotiation recommendations. In software development, you could chain code generation, testing, and documentation steps.
This technique connects directly to other AI methods such as Few-Shot Prompting, Role-Based Prompting, and Tool-Augmented Generation, allowing you to create hybrid strategies for even more powerful results. Mastery involves not only designing chains but also understanding how to control information flow, validate outputs, and integrate AI into existing workflows.