Prompt Chaining in 2026: The Reliable Way to Build Agents (Without 200-Line Prompts)
Definition (quote-ready): Prompt chaining is the practice of breaking a complex task into subtasks, then using the output of each step as the input to the next so you get more reliable, debuggable results. See the Prompt Engineering Guide for a deeper overview.
Why prompt chaining wins
Big prompts fail for two reasons: too many objectives and no checkpoints. Chaining adds checkpoints, improves controllability, and makes failures obvious.
- Reliability: each step has a narrow scope
- Debuggability: you can find the exact step that failed
- Consistency: structured handoffs reduce output drift
The 3 chain types you should use
1) Sequential chain (most common)
- Step 1: extract
- Step 2: transform
- Step 3: finalize
2) Evaluator-optimizer chain (quality mode)
Use a second step to critique and improve the result. Anthropic describes this iterative evaluator pattern in Building Effective AI Agents.
3) Branching chain (explore options)
- Generate 3 approaches
- Score them with a rubric
- Pick the best and finalize
A copy/paste chaining template (works across models)
This pattern aligns with agent workflows used in modern guides like the OpenAI Cookbook.
Step 1 - Extract
Extract only the facts relevant to solving the task.
Output: bullet list of facts + unknowns.
Step 2 - Plan
Create a plan using ONLY the extracted facts.
Output: numbered steps + assumptions.
Step 3 - Produce
Execute the plan.
Output must match: [format rules].
Step 4 - Evaluate
Check the output against this rubric: [rubric].
If it fails, rewrite once.
When to use prompt chaining
- The task has multiple phases (extract, plan, produce)
- You need auditable checkpoints
- Output quality matters more than speed
Common failure modes
- Steps are too big or too vague
- No schema between steps
- Missing evaluation rubric
- No stop criteria for rewrites
- Chaining simple tasks that do not need it
Rules of thumb
- Keep each step under 5 instructions
- Use a fixed output schema between steps
- Cache stable steps to reduce cost
- Add a single evaluator pass for quality work
FAQ
What problems does prompt chaining solve?
Reliability, debugging, and output consistency across complex tasks.
When should I not chain?
When the task is simple and you can evaluate it in one step.
Does chaining increase cost?
Yes, but prompt caching can reduce cost significantly for repeated steps.
How many steps should a chain have?
As few as possible. Start with 3 to 4 steps and only add steps when you need a new checkpoint.
How do I keep chain outputs stable over time?
Use prompt testing and versioning to catch drift and regressions.
Related resources
Last updated: December 2025.
