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

    Start using these tools today with Vincony's free Developer plan.

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