Introduction

Single LLM calls are rarely sufficient for complex tasks. Chaining — connecting multiple LLM calls in a pipeline — enables sophisticated workflows where each step builds on or refines the output of the previous one. This guide covers the essential chaining patterns used in production AI systems.

LLM Chaining and Pipeline Patterns

Why Chain?

A single LLM call has limitations:

  • Attention dilution : Long, complex prompts dilute attention across too many requirements

  • Error compounding : A single ambiguous instruction can produce incorrect output

  • Token waste : Including all context and instructions in one call is inefficient

  • Debugging difficulty : When output is wrong, isolating which instruction caused the problem is hard

Chaining addresses these by decomposing complex tasks into focused steps, each with a clear objective and validation criteria.

Core Patterns

Sequential Chain

The simplest pattern: output of step N becomes input to step N+1.

Use case : Multi-stage content processing

Raw text → Extract key facts → Verify facts → Format output

def sequential_chain(text):

facts = extract_facts(text)

verified = verify_facts(facts)

formatted = format_output(verified)

return formatted

def extract_facts(text):

return call_llm("Extract all factual claims from this text:", text)

def verify_facts(claims):

return call_llm("Verify each claim. Mark as VERIFIED, QUESTIONABLE, or FALSE:", claims)

def format_output(verified):

return call_llm("Format the verified claims as a clean bullet list:", verified)

Map-Reduce Chain

Process multiple items independently, then combine results.

Use case : Summarizing many documents, analyzing multiple customer reviews

def map_reduce(items, map_prompt, reduce_prompt):

Map: process each item independently

intermediate = []

for item in items:

result = call_llm(map_prompt, item)

intermediate.append(result)

Reduce: combine all intermediate results

combined = "\n---\n".join(intermediate)

final = call_llm(reduce_prompt, combined)

return final

Example: summarize 50 customer reviews

reviews = load_reviews()

map_prompt = "Summarize this customer review in one sentence, focusing on sentiment and key points:"

reduce_prompt = "Combine these review summaries into an overall analysis with common themes:"

analysis = map_reduce(reviews, map_prompt, reduce_prompt)

Parallel Processing

Run multiple independent chains simultaneously, then merge results.

Use case : Generating different sections of a document simultaneously

import asyncio

async def parallel_chain(topic):

intro, specs, pricing, conclusion = await asyncio.gather(

generate_intro(topic),

generate_specs(topic),

generate_pricing(topic),

generate_conclusion(topic)

)

return assemble_document(intro, specs, pricing, conclusion)

Parallel processing reduces wall-clock time significantly when chains are independent.

Routing Chain

Route input to different sub-chains based on classification.

Use case : Customer support ticket routing

def routing_chain(query):

First, classify the query type

category = classify_query(query)

Route to specialized handler

if category == "billing":

return billing_chain(query)

elif category == "technical":

return technical_support_chain(query)

elif category == "account":

return account_management_chain(query)

else:

return general_inquiry_chain(query)

def classify_query(query):

categories = call_llm("""

Classify this customer query into one of: billing, technical, account, general

Respond with only the category name.

""", query)

return categories.strip().lower()

Branching Chain

Pursue multiple investigation paths from a single input, then synthesize.

Use case : Research and analysis

Query

├→ Factual research chain (what are the known facts?)

├→ Analysis chain (what does this mean?)

├→ Stakeholder chain (who is affected?)

└→ Timeline chain (when did events occur?)

└→ Synthesis: combine all branches into comprehensive report

Validation Chain

Add verification steps between generation steps to catch errors early.

def generate_with_validation(topic):

draft = generate_draft(topic)

Validation gate

issues = validate_draft(draft)

if issues:

draft = revise_draft(draft, issues)

Re-validate

issues = validate_draft(draft)

if not issues:

return draft

If still has issues after revision, flag for human review

return {"draft": draft, "issues": issues, "needs_review": True}

def validate_draft(draft):

return call_llm("""

Check this draft for:

1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\. Factual accuracy

2\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\. Internal consistency

3\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\. Tone appropriateness

4\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\. Completeness

List any issues found. If none, respond with "NO ISSUES".

""", draft)

Advanced Patterns

Recursive Chain

Apply the same chain repeatedly until a condition is met:

def recursive_refine(text, max_iterations=5):

for i in range(max_iterations):

improved = call_llm("Improve this text: make it clearer and more concise:", text)

quality_score = evaluate_quality(improved)

if quality_score >= 0.9:

return improved

text = improved

return text

Feedback Loop Chain

Use the model's own output to identify and correct its mistakes:

def self_correcting_generation(task):

output = generate(task)

critique = call_llm("Critique this output. What's wrong or missing?", output)

if "nothing wrong" in critique.lower():

return output

revision = call_llm(f"Revise this output based on this feedback: {critique}", output)

return revision

Production Considerations

Error handling : Each chain step should have a timeout, retry logic, and fallback behavior.

Observability : Log inputs, outputs, latency, and token usage at each chain step. This is essential for debugging and cost optimization.

Caching : Cache results of deterministic chain steps (classification, extraction) to avoid redundant LLM calls.

Human escalation : Design chains so that when confidence is low or validation fails, the task escalates to a human operator.

Conclusion

LLM chaining transforms unreliable single-shot generation into reliable multi-step pipelines. Start with sequential chains for simple transformations, add map-reduce for batch processing, and incorporate routing and branching for complex workflows. The key principle: each step should do one thing well, with clear inputs, outputs, and validation criteria.