Structured Prompt Language: Declarative Context Management for LLMs
arXiv:2602.21257v1 Announce Type: new Abstract: We present SPL (Structured Prompt Language), a declarative SQL-inspired language that treats large language models as generative knowledge bases and their context windows as constrained resources. SPL provides explicit WITH BUDGET/LIMIT token management, an automatic query optimizer, EXPLAIN transparency analogous to SQL's EXPLAIN ANALYZE, and native integration of retrieval-augmented generation (RAG) and persistent memory in a single declarative framework. SPL-flow extends SPL into resilient agentic pipelines with a three-tier provider fallback strategy (Ollama -> OpenRouter -> self-healing retry) fully transparent to the .spl script. Five extensions demonstrate the paradigm's breadth: (1) Text2SPL (multilingual NL->SPL translation); (2) Mixture-of-Models (MoM) routing that dispatches each PROMPT to a domain-specialist model at runtime; (3) Logical Chunking, an intelligent strategy for documents exceeding a single context window--expres
arXiv:2602.21257v1 Announce Type: new Abstract: We present SPL (Structured Prompt Language), a declarative SQL-inspired language that treats large language models as generative knowledge bases and their context windows as constrained resources. SPL provides explicit WITH BUDGET/LIMIT token management, an automatic query optimizer, EXPLAIN transparency analogous to SQL's EXPLAIN ANALYZE, and native integration of retrieval-augmented generation (RAG) and persistent memory in a single declarative framework. SPL-flow extends SPL into resilient agentic pipelines with a three-tier provider fallback strategy (Ollama -> OpenRouter -> self-healing retry) fully transparent to the .spl script. Five extensions demonstrate the paradigm's breadth: (1) Text2SPL (multilingual NL->SPL translation); (2) Mixture-of-Models (MoM) routing that dispatches each PROMPT to a domain-specialist model at runtime; (3) Logical Chunking, an intelligent strategy for documents exceeding a single context window--expressed naturally through SPL's existing CTE syntax with no new constructs, decomposing a large query into a Map-Reduce pipeline that reduces attention cost from O(N^2) to O(N^2/k) and runs identically on cloud (parallel) or local hardware (sequential); (4) SPL-flow, a declarative agentic orchestration layer with resilient three-tier provider fallback; and (5) BENCHMARK for parallel multi-model comparison with automatic winner persistence. We provide a formal EBNF grammar, two pip-installable Python packages (spl-llm, spl-flow), and comparison against Prompty, DSPy, and LMQL. SPL reduces prompt boilerplate by 65% on average, surfaces a 68x cost spread across model tiers as a pre-execution signal, and runs the identical .spl script at $0.002 on OpenRouter or at zero marginal cost on a local Ollama instance--without modification.
Executive Summary
This article introduces Structured Prompt Language (SPL), a declarative SQL-inspired language designed to manage large language models (LLMs) as generative knowledge bases. SPL provides explicit token management, query optimization, transparency, and native integration of retrieval-augmented generation (RAG) and persistent memory. The authors demonstrate the paradigm's breadth with five extensions, including text-to-SPL translation, model routing, logical chunking, and a declarative agentic orchestration layer. SPL reduces prompt boilerplate by 65% and surfaces a 68x cost spread across model tiers as a pre-execution signal. The framework is released as two pip-installable Python packages and compared to existing solutions. This innovative approach has significant implications for the development and deployment of LLM-based applications, offering improved efficiency, transparency, and cost-effectiveness.
Key Points
- ▸ SPL provides a declarative SQL-inspired language for managing LLMs as generative knowledge bases
- ▸ SPL offers explicit token management, query optimization, transparency, and native integration of RAG and persistent memory
- ▸ The framework is demonstrated with five extensions, including text-to-SPL translation, model routing, and logical chunking
Merits
Strength in declarative approach
SPL's declarative nature allows for explicit and transparent management of LLMs, improving efficiency and reducing the risk of errors
Improved efficiency and cost-effectiveness
SPL's query optimization and token management features enable faster and more cost-effective execution of LLM-based applications
Native integration of RAG and persistent memory
SPL's native integration of retrieval-augmented generation and persistent memory enables more effective and efficient use of LLMs
Demerits
Limited support for complex queries
SPL's performance may be affected by complex queries, which may not be optimized effectively by the query optimizer
Dependence on specific LLMs and hardware
SPL's performance and functionality may be limited by the specific LLMs and hardware used, which may not be widely available
Expert Commentary
The introduction of SPL represents a significant advancement in the development of LLM-based applications. By providing a declarative language for managing LLMs as generative knowledge bases, SPL offers improved efficiency, transparency, and cost-effectiveness. The framework's native integration of RAG and persistent memory enables more effective and efficient use of LLMs, while its transparency and explainability features enable more effective use of LLMs in real-world applications. However, the framework's performance may be affected by complex queries, and its dependence on specific LLMs and hardware may limit its widespread adoption. Nevertheless, SPL has significant implications for the development and deployment of LLM-based applications, and its innovative approach has the potential to revolutionize the field.
Recommendations
- ✓ Researchers and developers should explore the application of SPL in various domains, including natural language processing, computer vision, and text-to-image synthesis
- ✓ The authors should further investigate the performance of SPL on complex queries and develop strategies to optimize its performance in such cases
