Making Prompts First-Class Citizens for Adaptive LLM Pipelines
Summary: Proposes SPEAR, treating prompts as first-class, structured, versioned artifacts integrated into execution for provenance, introspection, and reuse. Adds adaptive prompt refinement and policy-driven when‑then control to evolve prompts at runtime, unlocking optimization and integration opportunities in LLM pipelines. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Uğur Çetintemel (Brown University)
- 2. Shu Chen (Brown University)
- 3. Alexander W. Lee (Brown University)
- 4. Deepti Raghavan (Brown University)
- 5. Duo Lu (Brown University)
- 6. Andrew Crotty (Northwestern University)
BibTeX Citation
@inproceedings{cetintemel_cidr26,
address = {Amsterdam, Netherlands},
series = {{CIDR} '26},
title = {{Making Prompts First-Class Citizens for Adaptive LLM Pipelines}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Çetintemel, Uğur and Chen, Shu and Lee, Alexander W. and Raghavan, Deepti and Lu, Duo and Crotty, Andrew},
year = {2026}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,245 | Palimpzest: Optimizing AI-Powered Analytics with Declarative Query Processing | 2025 | CIDR | 0.00011507415 |
| 1,343 | DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing | 2025 | VLDB | 0.00011095866 |
| 4,081 | Abacus: A Cost-Based Optimizer for Semantic Operator Systems | 2026 | VLDB | 6.9165634e-05 |
| 4,621 | spade: Synthesizing Data Quality Assertions for Large Language Model Pipelines | 2024 | VLDB | 6.6024412e-05 |
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