
Prompt engineering is the process of writing prompts that guide artificial intelligence (AI) models (LLMs) generate desired outputs.
To get the best results from LLMs, two popular techniques are often used: Prompt Chaining and Chain-of-Thought (CoT) Prompting.
Each technique has its own strengths and serves different needs depending on the complexity and nature of the task.
In this post, we will explore these two approaches in detail to help you understand their capabilities and decide which one works best for your requirements.

Prompt Chaining involves breaking down a task into smaller, sequential prompts, with each prompt feeding into the next one. Each step in the chain addresses a specific part of the task, which leads to a refined outcome through iteration and improvement. This makes it particularly useful for tasks that need gradual refinement or contain multiple components.
Prompt Chaining is particularly helpful for:

Chain-of-Thought (CoT) Prompting allows large language models to solve complex tasks by breaking them into a sequence of logical steps within a single prompt. Unlike prompt chaining, CoT provides a step-by-step reasoning process in one go, making it particularly effective for tasks requiring explicit logical steps and structured reasoning.
Chain-of-Thought Prompting is best suited for:
| Aspect | Prompt Chaining | Chain-of-Thought (CoT) |
|---|---|---|
| Primary Function | Refining tasks through multiple prompts | Solving complex problems via detailed reasoning in a single prompt |
| Complexity Handling | Breaks down tasks into manageable subtasks | Tackles complex issues with structured, logical reasoning |
| Flexibility | High — can adjust each step independently | Limited — requires reworking the entire prompt for adjustments |
| Computational Cost | Lower — simpler prompts executed sequentially | Higher due to the detailed reasoning in one shot |
| Ideal Use Cases | Content creation, debugging, iterative learning | Logical reasoning, decision-making, multi-step analysis |
| Error Handling | Errors are easier to correct at each prompt stage | Errors require re-evaluation of entire reasoning |
| Autonomy | Dependent on individual prompts | More autonomous due to comprehensive reasoning |
Prompt Chaining and Chain-of-Thought (CoT) Prompting are important techniques for effectively using large language models (LLMs). Prompt Chaining breaks tasks into smaller steps, offering flexibility and the ability to refine each part, which is ideal for tasks like content creation and debugging.
CoT Prompting, on the other hand, is suited for tasks that require clear, logical reasoning. By outlining each step within a single prompt, it supports complex problem-solving and ensures a systematic approach.
For most cases, Combining both methods can enhance the performance of LLMs. Structuring a task with Prompt Chaining and then applying CoT Prompting for detailed reasoning leads to more precise and organized outcomes. Understanding when to use each technique allows you to achieve more accurate and useful results with prompt engineering.

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