What If LLM Workflow Could Be Orchestrated Like Writing SQL?

2026-06-23 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

Structured Prompt Language (SPL) extends its initial prompt and context management capabilities to orchestrate LLM workflows, mirroring SQL's separation of "what" from "how" in data engineering. This open-source language, alongside the Apache 2.0 Momagrid distributed inference grid, addresses common LLM workflow challenges: provider lock-in, unverified outputs, and high API costs/data privacy concerns. SPL uses a `.spl` file for logical specification and runtime `--adapter` flags for execution across 14 providers like Ollama, Claude, and OpenRouter. It introduces `GENERATE` (probabilistic LLM), `SOLVE` (deterministic kernel), and `ASSERT` (deterministic gate) constructs for auditable verification. A case study involving 10 models and 20 math problems across 4,700+ experimental cells demonstrated that a SymPy/Sage kernel caught significant errors LLMs confidently hid, with pass rates dropping from 100% (unverified) to 27%-92% (verified). Momagrid also enabled linear scaling, reducing a 60-minute benchmark to 20 minutes on 3 consumer GPUs.

Key takeaway

For MLOps Engineers building LLM-powered applications, adopting Structured Prompt Language (SPL) can significantly mitigate provider lock-in and enhance output reliability. By declaring workflows once and using runtime adapters, you can seamlessly switch between local, grid, or cloud LLMs, optimizing costs and data privacy. Integrate SPL's `SOLVE` and `ASSERT` constructs to embed deterministic verification, ensuring your LLM outputs are auditable and accurate, especially for critical tasks like mathematical computations.

Key insights

SPL orchestrates LLM workflows like SQL, separating "what" from "how" and integrating deterministic verification.

Principles

• Decouple workflow logic from LLM provider.
• Integrate deterministic kernels for output verification.
• Utilize distributed grids for cost-effective, private inference.

Method

SPL workflows declare `GENERATE` (LLM), `SOLVE` (deterministic kernel), and `ASSERT` (deterministic gate) steps. Runtime `--adapter` flags select execution engines.

In practice

• Use `--adapter` to switch LLM providers without code changes.
• Implement `SOLVE` and `ASSERT` for math or logic verification.
• Deploy Momagrid on idle consumer GPUs for local inference.

Topics

• Structured Prompt Language
• LLM Orchestration
• Distributed Inference
• Momagrid
• LLM Verification
• Neurosymbolic AI

Code references

• digital-duck/SPL.py
• digital-duck/momagrid

Best for: NLP Engineer, AI Architect, AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.