What is Prompt Chaining?
Prompt chaining splits a complex task into smaller, sequential steps. Each step uses a separate prompt that processes information and passes results to the next prompt in the chain. This approach allows AI systems to handle tasks that would be too complex or inconsistent if attempted in a single prompt.
How Prompt Chaining Works
In prompt chaining, you create a sequence of prompts where each prompt has a specific purpose. The AI processes the first prompt and generates an output. That output then becomes part of the input for the second prompt, and so on.
For example, if you want to analyze customer feedback, you might chain three prompts:
- Extract key themes from the feedback
- Categorize each theme as positive, negative, or neutral
- Generate actionable recommendations based on the categorized themes
Output A → [Prompt 2] → Output B
Output B → [Prompt 3] → Final Result
Why Prompt Chaining Matters
Prompt chaining improves accuracy because each prompt focuses on one specific task. When you try to do too much in a single prompt, the AI model can lose focus or produce inconsistent results.
Prompt chaining also makes workflows more transparent. You can see exactly what happened at each step, which makes debugging easier. If something goes wrong, you know which prompt in the chain needs adjustment.
Complex tasks benefit from prompt chaining because you can use different strategies for different parts of the workflow. One prompt might need detailed instructions while another works better with a simple question. This is particularly useful when working with LLM agents that need to make decisions at each step.
Example of Prompt Chaining
Here is a simple prompt chain for writing a product description:
Prompt 1: "List the key features of this product: [product details]"
Output 1: "Feature list: waterproof, 10-hour battery, wireless charging"
Prompt 2: "Take these features and identify the main customer benefit for each: [Output 1]"
Output 2: "Benefits: use in any weather, all-day usage, convenient charging"
Prompt 3: "Write a product description using these benefits: [Output 2]"
Output 3: "[Final product description]"
Common Mistakes with Prompt Chaining
The most common mistake is creating too many steps. Each additional prompt adds time and cost. If you can accomplish a task in two prompts instead of five, do that.
Another mistake is not validating outputs between steps. If an early prompt produces bad output, every subsequent prompt will work with that bad data. Always check critical outputs before passing them forward.
Poor error handling causes problems in prompt chains. If one prompt fails, you need a plan. Some implementations retry the failed prompt, others use fallback prompts, and some notify a human to intervene.
Related Concepts
Prompt chaining connects to several other AI techniques. LLM agents often use prompt chaining internally to break down tasks. Retrieval augmented generation can be part of a prompt chain where one prompt retrieves information and another uses it to generate a response.
Understanding fine-tuning helps because you might fine-tune a model to work better in a specific step of your chain. Embeddings are relevant when your chain needs to search or compare semantic similarity between steps.