Make provenance and evidence traceability first-class for dspy advanced prompting (state-of-the-art prompting techniques implementation dspy)

[haasonsaas]

Summary

Carry source, decision, and output provenance through the main workflow so downstream agents can audit and cite it.

This issue was generated from an org-wide EvalOps mining pass on 2026-05-10 07:57 UTC. It combines live GitHub repo signals with a per-repo arXiv search. Treat the research links as grounding for a concrete implementation, not as a request for a literature review.

Repo Evidence

• Repository description: State-of-the-art prompting techniques implementation with DSpy - Manager-style prompts, role personas, meta-prompting, and more
• Tree signals: 0 docs files, 0 workflows, 0 proto files, 1 test-like files.
• README.md:58 includes latent-spec language: ### 10. Evaluation Framework - Test cases more valuable than prompts
• README.md:255 includes latent-spec language: # Run evaluation framework python -m src.evaluations.evaluation_framework
• README.md:261 includes latent-spec language: Each technique includes built-in evaluation metrics: - Accuracy: How well the prompt performs its intended task
• README.md:288 includes latent-spec language: ### Building Evaluation Suites
• README.md:359 includes latent-spec language: 1. Prompts as Onboarding Docs: Treat prompts like you're onboarding a new employee 2. Test Cases > Prompts: Evaluation frameworks are more valuable than the prompts themselves 3. Uncertainty is Good: Better to admit uncertainty than hallucinate
• CONTRIBUTING.md:17 includes latent-spec language: - Add tests for new techniques - Update documentation as needed - Include examples in your implementations

Research Grounding

Repo axes: infra, governance, security, evaluation

Search keywords: prompts, techniques, evaluation, examples, api, dspy, src, prompt, import, uncertainty, your, test

• arXiv:2506.11019v1 Mind the Metrics: Patterns for Telemetry-Aware In-IDE AI Application Development using the Model Context Protocol (MCP) (Vincent Koc, Jacques Verre, Douglas Blank, Abigail Morgan), 2025.
• arXiv:2507.03620v1 Is It Time To Treat Prompts As Code? A Multi-Use Case Study For Prompt Optimization Using DSPy (Francisca Lemos, Victor Alves, Filipa Ferraz), 2025.
• arXiv:2412.15298v1 A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluation (Bhaskarjit Sarmah, Kriti Dutta, Anna Grigoryan, Sachin Tiwari, Stefano Pasquali, Dhagash Mehta), 2024.
• arXiv:2604.04869v1 Optimizing LLM Prompt Engineering with DSPy Based Declarative Learning (Shiek Ruksana, Sailesh Kiran Kurra, Thipparthi Sanjay Baradwaj), 2026.
• arXiv:2506.02032v2 Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges (Raj Patel, Himanshu Tripathi, Jasper Stone, Noorbakhsh Amiri Golilarz, Sudip Mittal, Shahram Rahimi), 2025.
• arXiv:2307.13473v1 Exploring MLOps Dynamics: An Experimental Analysis in a Real-World Machine Learning Project (Awadelrahman M. A. Ahmed), 2023.
• arXiv:2503.15577v1 Navigating MLOps: Insights into Maturity, Lifecycle, Tools, and Careers (Jasper Stone, Raj Patel, Farbod Ghiasi, Sudip Mittal, Shahram Rahimi), 2025.
• arXiv:2601.20415v1 An Empirical Evaluation of Modern MLOps Frameworks (Jon Marcos-Mercadé, Unai Lopez-Novoa, Mikel Egaña Aranguren), 2026.
• arXiv:2001.07935v2 CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking (Grigori Fursin, Herve Guillou, Nicolas Essayan), 2020.
• arXiv:2407.09107v1 MLOps: A Multiple Case Study in Industry 4.0 (Leonhard Faubel, Klaus Schmid), 2024.

What To Build

• Add stable identifiers for source records, derived decisions, and emitted outputs.
• Thread those identifiers through logs/events/API responses without leaking secrets.
• Provide a query or debug surface that reconstructs the chain for one completed workflow.

Acceptance Criteria

• A short design note names the repo-specific workflow, threat or correctness model, and the research assumptions being adopted.
• A runnable check, fixture, or verifier exercises the new contract in CI or an equivalent local command documented in the repo.
• The implementation emits or stores enough evidence for a downstream agent/operator to cite inputs, decisions, and outputs.
• At least one negative/degraded-mode case is covered so failures are observable rather than silently accepted.
• Documentation links the new behavior to the relevant EvalOps platform primitive or explicitly records why this repo remains standalone.

Notes

• Generated issue 2/5 for evalops/dspy-advanced-prompting by evalops_org_miner.py.
• Before implementation, confirm the sampled latent-spec snippets still match main; this issue intentionally cites exact file paths/lines where the mining pass saw them.