Prompting Techniques

Prompt Chaining

Quick Answer: A technique where the output of one prompt becomes the input for the next, creating a sequential pipeline of AI operations.

Prompt Chaining is a technique where the output of one prompt becomes the input for the next, creating a sequential pipeline of AI operations. Each step in the chain handles a focused sub-task, producing more reliable results than attempting complex tasks in a single prompt.

Example

Analyzing a legal contract in 3 chained steps: (1) 'Extract all obligations from this contract' -> list of obligations (2) 'Classify each obligation by risk level: high, medium, low' -> risk-tagged list (3) 'Write a summary memo of high-risk obligations for the legal team' -> final memo.

Why It Matters

Prompt chaining is how production AI systems handle complex tasks that a single prompt can't reliably solve. It's the manual predecessor to agentic AI. Designing effective chains is one of the most practically valuable prompt engineering skills.

How It Works

Prompt chaining breaks a complex task into a sequence of simpler sub-tasks, where each prompt handles one step and passes its output to the next prompt as input. This is more reliable than attempting complex tasks in a single prompt because each step can be focused, validated, and debugged independently.

A typical chain might involve: (1) extract relevant information from a document, (2) analyze the extracted information against criteria, (3) generate a recommendation based on the analysis, (4) format the recommendation for the target audience. Each step uses a different prompt optimized for that specific task.

Chaining strategies include sequential chains (linear A -> B -> C), branching chains (route to different prompts based on classification), and parallel chains (run multiple analyses simultaneously, then merge results). The choice depends on the task structure and whether intermediate results affect which subsequent steps are needed.

Common Mistakes

Common mistake: Creating chains that are too long, amplifying errors at each step

Keep chains to 3-5 steps maximum. Each step has a small error rate that compounds. Longer chains need intermediate validation checks.

Common mistake: Not validating intermediate outputs between chain steps

Add format and content validation between steps. If step 2's output doesn't match step 3's expected input format, catch it early rather than getting garbage at the end.

Career Relevance

Prompt chaining is a fundamental production skill for prompt engineers and AI engineers. It's the primary technique for building reliable AI workflows and is the conceptual foundation for more advanced agentic systems.

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