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<title>Appendix A: Chaining Prompts — Claude Prompt Engineering</title>
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<li class="sidebar__item active"><a href="app01-chaining-prompts.html"><span class="num">A</span> Chaining Prompts</a></li>
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<span>Appendix A</span>
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<div class="lesson-header__badge">
<span class="lesson-card__badge badge--appendix">Appendix</span>
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<h1 class="lesson-title">Chaining Prompts</h1>
<p class="lesson-subtitle">Single prompts have limits. Prompt chaining breaks complex tasks into a pipeline of focused steps — each prompt's output feeding the next as input. This is how production AI systems actually work.</p>
<div class="lesson-meta">
<span><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><circle cx="12" cy="12" r="10"/><polyline points="12 6 12 12 16 14"/></svg> 20 min read</span>
<span><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z"/></svg> 7 code examples</span>
<span>Appendix A of B</span>
</div>
</header>
<div class="lesson-body">
<h2>Why Single Prompts Have Limits</h2>
<p>A single prompt works well when a task can be completed in one focused step. But complex real-world tasks often involve multiple phases: researching, synthesizing, deciding, formatting, and validating. Cramming all of this into one prompt creates several problems:</p>
<div class="technique-grid">
<div class="technique-card">
<div class="technique-card__icon">📏</div>
<div class="technique-card__title">Context Window Limits</div>
<div class="technique-card__desc">A single prompt can't process more text than fits in Claude's context window. A chain can handle documents of any size by breaking them into chunks across sequential steps.</div>
</div>
<div class="technique-card">
<div class="technique-card__icon">🎯</div>
<div class="technique-card__title">Focus Dilution</div>
<div class="technique-card__desc">Asking Claude to do five things in one prompt reduces performance on each. A chain of five focused prompts — each doing one thing well — consistently outperforms one sprawling prompt.</div>
</div>
<div class="technique-card">
<div class="technique-card__icon">🔍</div>
<div class="technique-card__title">No Intermediate Validation</div>
<div class="technique-card__desc">A single prompt fails silently. In a chain, you can inspect and validate each intermediate output — catching errors before they propagate and corrupt downstream steps.</div>
</div>
<div class="technique-card">
<div class="technique-card__icon">🔄</div>
<div class="technique-card__title">No Conditional Logic</div>
<div class="technique-card__desc">Single prompts can't branch. Chains can: inspect the output of Step 1, then decide whether to run Step 2A or Step 2B based on what Claude found.</div>
</div>
</div>
<h2>The Pipeline Metaphor</h2>
<p>Think of prompt chaining as a data pipeline. Each stage transforms the data and passes it downstream:</p>
<div class="code-block">
<div class="code-block__header">
<span class="code-block__label">Pipeline Visualization</span>
</div>
<pre><code">Input Document
│
▼
[STEP 1: Extract] → "What are the key entities and facts?"
│ Output: structured JSON with extracted data
▼
[STEP 2: Analyze] → "Analyze these facts. What are the implications?"
│ Output: prose analysis
▼
[STEP 3: Format] → "Format this analysis as an executive brief"
│ Output: formatted document
▼
[STEP 4: Validate] → "Check: does this brief match the original facts?"
│ Output: validation report or approved flag
▼
Final Output</code></pre>
</div>
<h2>Types of Chains</h2>
<div class="technique-grid">
<div class="technique-card">
<div class="technique-card__icon">🔗</div>
<div class="technique-card__title">Refinement Chains</div>
<div class="technique-card__desc">Generate → Critique → Improve. Use when quality matters more than speed. Each step makes the previous output better: draft → editorial feedback → revised draft.</div>
</div>
<div class="technique-card">
<div class="technique-card__icon">🔬</div>
<div class="technique-card__title">Analysis Chains</div>
<div class="technique-card__desc">Extract → Analyze → Synthesize. For processing complex information sources. Break analysis into: what does it say? → what does it mean? → what should we do?</div>
</div>
<div class="technique-card">
<div class="technique-card__icon">🌿</div>
<div class="technique-card__title">Decision Chains</div>
<div class="technique-card__desc">Classify → Branch. Step 1 categorizes the input; subsequent steps handle each category differently. Essential for building routing logic in AI applications.</div>
</div>
<div class="technique-card">
<div class="technique-card__icon">⚡</div>
<div class="technique-card__title">Parallel Chains</div>
<div class="technique-card__desc">Run multiple independent prompts simultaneously using async/threading. Collect all results and pass to a synthesis step. Dramatically reduces latency for multi-angle analysis.</div>
</div>
</div>
<h2>Sequential Chain: Research → Summarize → Format</h2>
<div class="code-block">
<div class="code-block__header">
<span class="code-block__label">Research Pipeline (Python)</span>
<button class="code-block__copy">Copy</button>
</div>
<pre><code">import anthropic
client = anthropic.Anthropic()
def call_claude(system: str, user: str, max_tokens: int = 1024) -> str:
response = client.messages.create(
model="claude-opus-4-5",
max_tokens=max_tokens,
system=system,
messages=[{"role": "user", "content": user}]
)
return response.content[0].text
def research_pipeline(topic: str, raw_sources: list[str]) -> str:
"""3-step chain: extract key info → analyze → produce executive brief."""
# STEP 1: Extract key facts from each source
extracted_facts = []
for i, source in enumerate(raw_sources):
facts = call_claude(
system="You are a research analyst. Extract key facts, dates, and statistics from documents.",
user=f"Extract all key facts from this source. Return as a JSON array of strings.\n\n<source id='{i+1}'>\n{source}\n</source>"
)
extracted_facts.append(facts)
facts_combined = "\n".join([f"Source {i+1}: {f}" for i, f in enumerate(extracted_facts)])
# STEP 2: Analyze and synthesize
analysis = call_claude(
system="You are a strategic analyst. Synthesize information from multiple sources into coherent insights.",
user=f"""Analyze these extracted facts about '{topic}'.
Identify: key themes, conflicting information, important gaps.
<facts>
{facts_combined}
</facts>
Return: 3-5 key insights in prose.""",
max_tokens=2048
)
# STEP 3: Format as executive brief
brief = call_claude(
system="You are a senior business writer specializing in executive communications.",
user=f"""Format the following analysis as a one-page executive brief about '{topic}'.
Structure: Background (2 sentences) → Key Findings (3 bullet points) → Implications (2 sentences) → Recommended Actions (3 bullet points)
<analysis>
{analysis}
</analysis>""",
max_tokens=1024
)
return brief
# Usage
brief = research_pipeline("AI adoption in healthcare", [doc1, doc2, doc3])
print(brief)</code></pre>
</div>
<h2>Document Extract → Validate → Output Chain</h2>
<div class="code-block">
<div class="code-block__header">
<span class="code-block__label">Extraction with Validation</span>
<button class="code-block__copy">Copy</button>
</div>
<pre><code">import json
def extract_and_validate(contract_text: str) -> dict:
"""Extract contract data and validate the extraction before returning."""
# Step 1: Extract structured data
extraction_prompt = f"""Extract the following fields from this contract.
Return valid JSON only — no explanation.
Schema:
{{
"parties": ["list of party names"],
"effective_date": "YYYY-MM-DD or null",
"termination_date": "YYYY-MM-DD or null",
"payment_terms_days": number or null,
"liability_cap": number or null,
"governing_law": "state/jurisdiction or null"
}}
<contract>
{contract_text}
</contract>"""
raw_extraction = call_claude("You extract structured data from legal documents.", extraction_prompt)
try:
extracted = json.loads(raw_extraction)
except json.JSONDecodeError:
return {"error": "extraction_failed", "raw": raw_extraction}
# Step 2: Validate the extraction
validation_prompt = f"""Review this data extraction from a contract.
Verify: (1) are the field values consistent with the contract text?
(2) are any fields incorrectly null that should have values?
(3) are any field values wrong?
Return JSON: {{"valid": true/false, "issues": ["list of issues found"], "corrections": {{}}}}
<contract>{contract_text[:2000]}</contract>
<extraction>{json.dumps(extracted)}</extraction>"""
validation = call_claude("You validate data extractions for accuracy.", validation_prompt)
val_data = json.loads(validation)
if not val_data.get("valid") and val_data.get("corrections"):
extracted.update(val_data["corrections"])
return {"data": extracted, "validation": val_data}
</code></pre>
</div>
<h2>Parallel Chains: Running Multiple Prompts Simultaneously</h2>
<div class="code-block">
<div class="code-block__header">
<span class="code-block__label">Parallel Analysis Chain (asyncio)</span>
<button class="code-block__copy">Copy</button>
</div>
<pre><code">import asyncio
import anthropic
async_client = anthropic.AsyncAnthropic()
async def analyze_from_perspective(topic: str, perspective: str) -> dict:
response = await async_client.messages.create(
model="claude-opus-4-5",
max_tokens=512,
messages=[{
"role": "user",
"content": f"Analyze '{topic}' from the perspective of {perspective}. 3 key points."
}]
)
return {"perspective": perspective, "analysis": response.content[0].text}
async def multi_perspective_analysis(topic: str) -> str:
# Run all perspectives in parallel
perspectives = ["a CFO", "an operations manager", "a frontline employee", "a customer"]
tasks = [analyze_from_perspective(topic, p) for p in perspectives]
results = await asyncio.gather(*tasks)
# Synthesize all perspectives into one coherent view
combined = "\n".join([f"{r['perspective']}: {r['analysis']}" for r in results])
synthesis_response = await async_client.messages.create(
model="claude-opus-4-5",
max_tokens=1024,
messages=[{
"role": "user",
"content": f"Synthesize these four perspectives on '{topic}' into a balanced summary.\n\n{combined}"
}]
)
return synthesis_response.content[0].text
# Usage
result = asyncio.run(multi_perspective_analysis("moving to a 4-day work week"))</code></pre>
</div>
<h2>State Management Between Prompts</h2>
<p>Each prompt in a chain receives only what you explicitly pass to it — there's no shared memory between API calls. You must actively manage state:</p>
<div class="technique-grid">
<div class="technique-card">
<div class="technique-card__icon">📦</div>
<div class="technique-card__title">Pass Full Context</div>
<div class="technique-card__desc">The simplest approach: pass the full accumulated context to each step. Works for short chains with small outputs. Becomes expensive and hit context limits for long chains.</div>
</div>
<div class="technique-card">
<div class="technique-card__icon">🗜️</div>
<div class="technique-card__title">Compress Between Steps</div>
<div class="technique-card__desc">Add a compression step between long steps: "Summarize the key findings from this analysis in 200 words." Pass the summary rather than the full output to the next step.</div>
</div>
<div class="technique-card">
<div class="technique-card__icon">📊</div>
<div class="technique-card__title">Structured State Object</div>
<div class="technique-card__desc">Use a JSON state object that accumulates outputs. Each step adds its results to the state object. The final step receives the full structured state and produces the final output.</div>
</div>
</div>
<h2>Best Practices for Chain Design</h2>
<div class="steps">
<div class="step">
<div class="step__num">1</div>
<div class="step__content">
<div class="step__title">Atomic prompts: one job per step</div>
<div class="step__desc">Each step in the chain should do exactly one thing well. If you find yourself writing "and then also..." in a step's instructions, split it into two steps.</div>
</div>
</div>
<div class="step">
<div class="step__num">2</div>
<div class="step__content">
<div class="step__title">Design for structured handoffs</div>
<div class="step__desc">Each step's output should be in a format that makes it easy to inject into the next step. Prefer JSON or XML for intermediate outputs — they're easy to pass as template variables.</div>
</div>
</div>
<div class="step">
<div class="step__num">3</div>
<div class="step__content">
<div class="step__title">Add validation steps for high-stakes chains</div>
<div class="step__desc">After any step that makes a critical decision (classification, extraction, analysis), add a validation step that checks the output for correctness before passing it downstream.</div>
</div>
</div>
<div class="step">
<div class="step__num">4</div>
<div class="step__content">
<div class="step__title">Handle errors gracefully</div>
<div class="step__desc">Wrap each step in try/except. Never let a parsing error in Step 2 crash the whole pipeline. Log errors, attempt recovery, or fail gracefully with informative error messages.</div>
</div>
</div>
</div>
<div class="callout callout--tip">
<div class="callout__icon">✅</div>
<div>
<div class="callout__title">Appendix A Takeaway</div>
<div class="callout__body">Prompt chaining is the foundational pattern for production AI systems. Break complex tasks into atomic, focused steps. Use structured (JSON/XML) outputs for clean handoffs between steps. Add validation steps for high-stakes decisions. Use parallel chains with asyncio when steps are independent, to minimize total latency. When single prompts fail, the answer is usually "break it into a chain."</div>
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