Prompt chaining and AI workflows: building multi-step LLM pipelines for complex tasks

Dev.to / 6/9/2026

💬 OpinionDeveloper Stack & InfrastructureTools & Practical Usage

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Key Points

The article explains how prompt chaining and multi-step LLM pipelines can be used to tackle complex tasks by structuring AI interactions as a workflow rather than a single prompt.
It emphasizes that successful AI integration is not only a technical problem but also involves defining requirements, measurable outcomes, and considering team and user needs.
It recommends starting with a simple end-to-end implementation, then iterating and improving once the pipeline works reliably.
It highlights the importance of thorough testing (including edge cases and failure scenarios) and ongoing production monitoring with metrics and alerts for human intervention.
It covers common pitfalls such as underestimating hidden complexity, over-engineering for scale too early, and accumulating technical debt without a deliberate plan to address it.

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