RAJARAJAN GANESAN

DOI: https://doi.org/10.5281/zenodo.21854043

Token consumption, not model capability, has become the binding operational constraint on production large language model (LLM) systems: system prompts accumulate instructions over months of iteration, retrieval-augmented generation (RAG) pipelines over-retrieve context, and multi-turn conversations are frequently replayed in full on every call. This paper presents a catalog of token-optimization design patterns for enterprise LLM deployments structural compression, semantic compression, prompt and prefix caching, retrieval scoping, conversation summarization, and output token control and specifies how each pattern can be composed with a deterministic business logic guardrail layer without weakening policy enforcement. Building on our prior work specifying a guardrail-mediated function-calling architecture for voice-driven enterprise resource planning (ERP) interaction [1], we extend the guardrail placement question to the token-optimization setting, where compression and caching decisions must not be allowed to remove or corrupt the content that guardrails depend on for enforcement. On a corpus of 2,150 production-style enterprise support and query tasks, the proposed pipeline reduces effective billed input tokens by 90.2% (4,820 to 470 tokens per request) and blended cost by 89.5%, while retaining 94.7% of baseline task accuracy at a 4x compression ratio and reducing unblocked guardrail policy violations from 0.41% to 0.06%. The results indicate that token optimization and policy enforcement are not competing objectives when compression is applied above, rather than inside, the guardrail boundary.

prompt engineering; token optimization; large language models; prompt compression; prompt caching; retrieval-augmented generation; business logic guardrails; enterprise AI; LLM cost engineering; production AI systems

This paper cataloged six token-optimization design patterns in current use for production LLM systems, proposed a layering principle that places compression and caching strictly above a deterministic business-logic guardrail boundary, and extended the guardrail-separation architecture developed in our prior work on voice-driven ERP function calling [1] to the token-optimization setting. Evaluation on 2,150 production-style enterprise tasks showed a 90.2% reduction in effective billed input tokens and an 89.5% reduction in blended cost, while retaining 94.7% of baseline task accuracy at a 4x compression ratio and, notably, reducing rather than increasing the rate of unblocked guardrail policy violations. These results support the conclusion that token optimization and policy enforcement are complementary rather than competing objectives when the two concerns are designed jointly, with guardrail-critical content explicitly exempted from lossy compression.

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RAJARAJAN GANESAN (2026). Token-Optimized Prompt Engineering at Enterprise Scale: Design Patterns for Production LLM Systems with Business Logic Guardrails. International Journal of Computer Techniques, 13(4), 177–192. ISSN: 2394-2231. DOI: https://doi.org/10.5281/zenodo.21854043