When to Chain Prompts

Not every task benefits from chaining. Learning to recognize when chaining adds value versus when it adds unnecessary complexity is a crucial skill.

Clear Indicators for Chaining

1. Tasks with Distinct Phases

If your task naturally breaks into phases with different requirements, chain:

Example: Document Processing

• Phase 1: Extract information (needs the full document)
• Phase 2: Validate extracted data (needs just the extracted data)
• Phase 3: Transform to target format (needs validated data + format spec)

Each phase has different context needs and success criteria.

2. Quality-Critical Outputs

When quality matters more than speed, add review steps:

Generate → Review → Revise → Final Check

This review step couldn't happen if generation and review were combined.

3. Tasks Requiring External Data

When you need to incorporate information from outside the prompt:

Generate Query → Fetch Data → Process with Data → Generate Output

Even if you're simulating the data fetch, separating this step makes the workflow realistic and testable.

4. Long or Complex Outputs

For outputs longer than ~1000 words, breaking into sections usually improves quality:

Create Outline → Write Section 1 → Write Section 2 → ... → Combine → Edit

5. Tasks with Validation Requirements

If you need to verify outputs meet specific criteria:

Generate → Validate Format → Validate Content → Approve or Retry

When NOT to Chain

Simple Transformations

If the task is straightforward, a single prompt is more efficient:

• Translating a paragraph
• Summarizing a short document
• Converting between data formats
• Answering a factual question

When Speed is Critical

Each chain step adds latency. For real-time applications, minimize steps:

• Chatbot responses
• Live coding assistance
• Interactive Q&A

When Context is Unified

If all the information needed fits comfortably in one prompt and the task is cohesive, don't artificially split it:

Don't chain: Analyzing a short product review for sentiment Do chain: Analyzing 50 reviews, categorizing issues, and generating a response strategy

The Decision Framework

Ask these questions:

Question	If Yes	If No
Does the task have distinct phases?	Consider chaining	Might be single prompt
Do later steps need to validate earlier steps?	Chain	Single prompt okay
Is the output longer than 1000 words?	Consider chaining sections	Single prompt okay
Do you need to incorporate external data?	Chain around data fetches	Single prompt okay
Is this time-critical (< 2 seconds)?	Minimize chain length	Chain freely
Could errors in early steps cascade?	Chain with validation	Single prompt okay

Hybrid Approaches

Sometimes the answer is a short chain rather than no chain or a complex chain:

Two-Step Chains

Many workflows benefit from just two steps:

Generate and Review

Step 1: Generate initial output
Step 2: Review and improve the output

Analyze and Act

Step 1: Analyze the situation
Step 2: Generate recommendations based on analysis

Extract and Transform

Step 1: Extract relevant information
Step 2: Transform into target format

Exercise: Chain or Not?

For each scenario, decide if you would chain and why:

Suggested answers:

1. SINGLE - Simple, direct transformation
2. CHAIN - Long output benefits from section-by-section generation
3. SINGLE - Factual question with clear answer
4. CHAIN - Distinct phases: analyze, identify, draft
5. SINGLE - Short input, direct task
6. CHAIN - Three distinct operations with validation between them

Cost-Benefit Analysis

Chaining has costs:

• Latency: Each step adds processing time
• Complexity: More steps mean more things that can break
• Token usage: Context often gets repeated between steps

Chaining has benefits:

• Quality: Focused steps produce better outputs
• Reliability: Validation catches errors early
• Flexibility: Easier to modify individual steps
• Debuggability: Can identify where problems occur

The rule: Chain when the quality/reliability benefits outweigh the latency/complexity costs.

Key Takeaways

• Chain when tasks have distinct phases with different requirements
• Chain when you need validation between steps
• Chain when outputs are long or complex
• Don't chain simple, direct transformations
• Consider two-step chains as a middle ground
• Always weigh quality benefits against complexity costs

In the next module, we'll dive into the mechanics of actually connecting prompts together.