Chain Design Principles
Effective prompt chains follow several principles: each step should have a single, clear objective; the output of each step should be verifiable before passing to the next; steps should be ordered from general to specific; and each step should include only the context it needs.
A common chain for document processing: (1) classify the document type, (2) extract relevant fields based on the type, (3) validate the extraction, (4) format the output. Each step is simple and focused.
Validation Gates
Between chain steps, insert validation gates that check the quality of intermediate output. If a step produces low-quality output, the gate can trigger a retry, a fallback, or an error. Validation gates prevent error propagation — a mistake in step 1 doesn't corrupt all subsequent steps.
Validation can be programmatic (check JSON structure, value ranges, required fields) or model-based (ask Claude to evaluate the quality of the output). Programmatic validation is faster and more reliable for structural checks.
Context Management in Chains
Each step in a chain gets its own API call with its own context. This means you control exactly what information each step receives. Pass only the relevant output from the previous step, not the entire conversation history.
This selective context passing is a key advantage of chaining over single prompts. Each step can have a specialized system prompt without the complexity of a multi-task prompt. It also means each step uses fewer tokens.
Key Concept
Each Step Should Be Independently Testable
The strength of prompt chaining is modularity. Each step should be independently testable with its own inputs and expected outputs. If a chain produces bad results, you can isolate which step failed and fix it without affecting other steps. This is the same principle as function decomposition in programming — small, focused, testable units.
Exam Traps
Chaining when a single prompt suffices
Chaining adds latency and cost (multiple API calls). If the task is simple enough for one prompt, chaining adds unnecessary complexity. The exam tests whether you can identify when chaining is warranted.
Passing too much context between steps
Each step should receive only the output it needs from the previous step, not the full history. Passing everything wastes tokens and can confuse the model.
Not validating between steps
Without validation gates, errors propagate and compound. A bad extraction in step 1 leads to a bad summary in step 2. The exam expects you to include validation.
Check Your Understanding
You need to process customer feedback: extract sentiment, identify topics, and generate a response. The topics determine the response template. Which chain design is most appropriate?
Build Exercise
Build a Prompt Chain
What you'll learn
- Design a multi-step prompt chain
- Implement validation gates between steps
- Manage context passing between steps
- Compare chain performance vs. single prompt
Design a 3-step chain for a specific task (e.g., analyze code: step 1 classify language and framework, step 2 identify issues, step 3 suggest fixes). Define inputs and outputs for each step.
WHY: Explicit input/output contracts make each step independently testable.
YOU SHOULD SEE: A clear chain design with typed inputs and outputs for each step.
Implement step 1 with its own system prompt and test it independently with 5 sample inputs.
WHY: Independent testing validates each step before assembling the chain.
YOU SHOULD SEE: Step 1 produces correct output for all test inputs.
Implement a validation gate between step 1 and step 2 that checks the output format and rejects invalid classifications.
WHY: Validation gates prevent bad intermediate results from corrupting subsequent steps.
YOU SHOULD SEE: Invalid step 1 output triggers a retry or error before reaching step 2.
Implement steps 2 and 3, wire the chain together, and test end-to-end with sample inputs.
WHY: End-to-end testing validates the complete chain including context passing and validation.
YOU SHOULD SEE: The chain processes input through all steps and produces the final output.
Sources
- Prompt Engineering: Chaining— Anthropic Documentation
- Building Effective Agents— Anthropic Documentation