Jan 25, 2026 7 min
Prompt Chaining: Build Complex AI Workflows Step by Step
Chain multiple AI prompts together to build sophisticated workflows — research → analyze → draft → review — all automated.
Prompt Engineering Workflows
Beyond Single Prompts
Complex tasks rarely fit in a single prompt. Prompt chaining breaks work into steps where each prompt's output feeds the next — like a pipeline for AI reasoning.
Building a Chain
import vincony
client = vincony.Client(api_key="YOUR_API_KEY")
chain = client.chains.create(
name="blog_research_pipeline",
steps=[
{
"name": "research",
"prompt": "Research the topic: {topic}. Find key facts and statistics.",
"model": "sonar-pro",
"output_key": "research_data"
},
{
"name": "outline",
"prompt": "Create a blog outline based on: {research_data}",
"model": "claude-opus",
"output_key": "outline"
},
{
"name": "draft",
"prompt": "Write a 1500-word blog post following: {outline}",
"model": "gpt-5",
"output_key": "draft"
},
{
"name": "review",
"prompt": "Proofread and improve: {draft}",
"model": "claude-opus",
"output_key": "final_article"
}
]
)
result = chain.run(topic="AI code review best practices")
print(f"Steps completed: {len(result.steps)}")
print(f"Total tokens: {result.total_tokens}")
print(result.final_article)Conditional Branching
Add conditions to your chains — if a fact-check fails, route to a correction step. If sentiment is negative, trigger a different response template.
Error Handling & Retries
Built-in retry logic, fallback models, and step-level error handling ensure your chains complete reliably even when individual steps fail.
# Chain with error handling
chain = client.chains.create(
name="robust_pipeline",
steps=[...],
error_handling={
"retry_count": 3,
"fallback_model": "gpt-4o",
"on_failure": "skip_and_continue"
},
timeout_seconds=120
)Pricing
Each step in a chain uses standard API credits. Chain orchestration is free — you only pay for the AI calls.
Try It Free — 100 API Credits
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