Think step by step, build step by step
Chain of thought
Chain of thought prompting asks the model to reason step by step before giving a final answer. Adding 'let's think step by step' to a prompt can measurably improve accuracy on reasoning tasks. It is one of the most popular techniques in production AI systems.
Prompt chaining separates steps
Instead of one long prompt with multiple instructions, you break the task into a chain: prompt 1 extracts key issues, its output feeds into prompt 2 which drafts an outline, which feeds into prompt 3 which writes the final response. Each link does one thing.
Chaining enables debugging
The biggest advantage of chaining is not performance — it is control. If the final email is bad, you can check each intermediate step and find exactly where the chain broke. With a single prompt, everything is mixed together and you cannot trace the failure.
The latency trade-off
Chains add latency because each step waits for the previous one. For real-time applications like search, short chains or single prompts may be necessary. For slower workflows like report generation, the debugging advantage of chaining usually wins.
Think of a task that requires multiple steps. What would go wrong if you tried to do it all in one prompt?