Q1. What is prompt chaining?
Prompt chaining is a technique where the output of one LLM call becomes the input (or part of the prompt) for a subsequent LLM call.
Instead of trying to solve a complex task in one go, you break it into smaller, manageable steps.
Each step uses a separate prompt tailored to that sub-task.
Example: Summarize a long article → Extract key entities → Generate a headline.
Chaining allows you to decompose complexity, verify intermediate results, and reduce errors by isolating failure points.
Instead of trying to solve a complex task in one go, you break it into smaller, manageable steps.
Each step uses a separate prompt tailored to that sub-task.
Example: Summarize a long article → Extract key entities → Generate a headline.
Chaining allows you to decompose complexity, verify intermediate results, and reduce errors by isolating failure points.
Q2. How does prompt chaining differ from a single complex prompt?
A single complex prompt asks the LLM to perform multiple tasks in one request; the model may mix up steps or produce inconsistent output.
Prompt chaining splits the work into separate steps.
Advantages: better control, easier debugging, ability to insert human validation between steps, and each prompt can be optimized independently.
Disadvantage: more API calls and latency.
Prompt chaining splits the work into separate steps.
Advantages: better control, easier debugging, ability to insert human validation between steps, and each prompt can be optimized independently.
Disadvantage: more API calls and latency.
Q3. What are common use cases for prompt chaining?
Common use cases include:
• document processing (OCR → cleaning → entity extraction)
• conversational agents (intent → slot filling → response)
• code generation (plan → write → test → refactor)
• data transformation (CSV → JSON → validation)
• multi-modal pipelines
• research paper analysis.
Any workflow that can be decomposed into sequential subtasks benefits from chaining.
• document processing (OCR → cleaning → entity extraction)
• conversational agents (intent → slot filling → response)
• code generation (plan → write → test → refactor)
• data transformation (CSV → JSON → validation)
• multi-modal pipelines
• research paper analysis.
Any workflow that can be decomposed into sequential subtasks benefits from chaining.
Q4. How do you handle errors and validation in a prompt chain?
Insert validation steps between chain links.
Example: generate intermediate output in JSON, parse it, if invalid then re-prompt with a correction instruction or use a fallback.
Only pass validated output to the next step.
You can also use a separate LLM call to verify correctness.
This makes the chain robust to individual component failures. In production, add timeouts and retry limits.
Example: generate intermediate output in JSON, parse it, if invalid then re-prompt with a correction instruction or use a fallback.
Only pass validated output to the next step.
You can also use a separate LLM call to verify correctness.
This makes the chain robust to individual component failures. In production, add timeouts and retry limits.
Q5. Give a concrete example of a prompt chain for customer support automation.
Goal: Classify and respond to customer emails.
Chain steps:
• Intent classification (billing, technical, account).
• If billing, extract invoice number and problem description as JSON.
• Generate response using extracted info: write a polite email apologizing for the issue.
• Optional safety check: verify that the response contains no unkeepable promises.
Each step is independent, and you can test or modify each without affecting others.
Chain steps:
• Intent classification (billing, technical, account).
• If billing, extract invoice number and problem description as JSON.
• Generate response using extracted info: write a polite email apologizing for the issue.
• Optional safety check: verify that the response contains no unkeepable promises.
Each step is independent, and you can test or modify each without affecting others.