Output-to-Input Pipelines
How to structure prompt outputs so they feed cleanly into the next step.
The biggest failure point in prompt chains is the handoff between steps. If Step 1's output isn't structured for Step 2's input, information gets lost or garbled. Designing clean interfaces between prompts is just as important as writing the prompts themselves.
The most reliable way to pass information between prompts is through structured formats. JSON is ideal for programmatic chains, while markdown with clear headings works for manual chains.
- Define the output schema in your prompt: "Output your analysis as JSON with these exact keys: ..."
- Include only what the next step needs — don't pass the full output if only a summary is needed
- Use consistent naming: if Step 1 calls it "key_findings," Step 2 should reference "key_findings," not "main_results"
- Add a validation step: ask the model to verify its output matches the required schema before finalizing
Structured Output Step
Forces structured output that feeds cleanly into the next step.
Analyze [INPUT] and produce output in this exact JSON structure:
```json
{
"summary": "2-3 sentence overview",
"findings": [
{
"finding": "description",
"evidence": "supporting data",
"confidence": "high|medium|low",
"implication": "what this means"
}
],
"next_steps": ["action item 1", "action item 2"]
}
```
Rules:
- Include exactly 3-5 findings
- Every field must be filled — no nulls or empty strings
- Confidence must be one of: high, medium, low
- Verify your JSON is valid before respondingManual chains are when you copy-paste output between prompts yourself. Programmatic chains use code to automate the pipeline. Both are valid approaches:
- Manual chains: Good for exploration, one-off tasks, and when you want to review and edit intermediate results
- Programmatic chains: Essential for production, repeated tasks, and when consistency matters. Use LangChain, LlamaIndex, or simple API scripts.
- Hybrid: Run manually first to validate the chain works, then automate once you're satisfied
Prompt Templates
Chain Input Formatter
Cleans and reformats output from one step for use in the next.
I'm going to provide the output from a previous analysis step. Your job is to: 1. Extract the key information needed for [NEXT TASK] 2. Reformat it as structured input 3. Flag any missing information that [NEXT TASK] will need Previous step output: [PASTE OUTPUT] Required format for next step: [DESCRIBE FORMAT]
Pipeline Validator
Quality gate between chain steps to catch errors before they propagate.
Validate this chain output before passing it to the next step: ```json [PASTE JSON OUTPUT] ``` Check: 1. Is the JSON valid and parseable? 2. Are all required fields present and non-empty? 3. Do values match expected types and ranges? 4. Is the data internally consistent? If issues found, output a corrected version. If clean, respond with "VALID" and the unchanged JSON.
Test Your Knowledge
Knowledge Check
1 / 2
What is the biggest failure point in prompt chains?
Key Takeaways
- ✓The handoff between prompts is the most common failure point in chains
- ✓Use structured output formats (JSON, defined markdown) for reliable data passing
- ✓Define the exact output schema in your prompt and include validation instructions
- ✓Build chains manually first to validate, then automate for production
- ✓Pass only what the next step needs — trim unnecessary information at each handoff
Continue Learning
Breaking Down Complex Tasks
Why one giant prompt often fails and how to decompose tasks for better results.
Error Handling in Chains
What to do when a step in your chain fails or produces poor output.
What Is Chain-of-Thought Prompting?
Understand the technique that dramatically improves AI reasoning on complex problems.