From Prompt Engineering to Prompt Systems: How Enterprises Should Think About LLM Inputs

The article discusses the limitations of treating prompts as one-off inputs and the need for enterprises to adopt a more systematic approach called 'prompt systems' to effectively leverage large language models (LLMs).

Details

The article explains that while prompt engineering works well for experiments and personal workflows, it struggles to scale in enterprise environments where prompts serve as infrastructure for multiple teams, evolving compliance rules, and dynamic contexts. To address this, the article introduces the concept of 'prompt systems' - a structured approach to managing LLM inputs that includes clear role definitions, modular instructions, dynamic context injection, guardrails and constraints, and versioning/observability. The key is to move from crafting individual prompts to designing prompt architectures. Layered prompts with different components (intent, instructions, context, user input, output constraints) are more resilient and easier to maintain than single, monolithic prompts. The article also highlights the importance of Retrieval-Augmented Generation (RAG) as a core part of modern prompt systems, enabling dynamic knowledge injection rather than static prompt content. Overall, the article emphasizes that for enterprises, prompts don't just guide models - they shape outcomes, and a systematic approach is necessary to leverage the power of LLMs responsibly at scale.