What is the recommended approach for setting up continuous integration and continuous deployment pipelines for prompt engineering assets and LLM evaluation suites?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What is the recommended approach for setting up continuous integration and continuous deployment pipelines for prompt engineering assets and LLM evaluation suites?
1 real search · 1 real-search pass
LLMOps prompt engineering CI/CD pipeline evaluation regression testing best practices prompt versioning deploymentGoogle ↗
OBSERVEDInstruction
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
LLMOps prompt engineering CI/CD pipeline evaluation regression testing best practices prompt versioning deployment Google ↗
- Prompt CI/CD: version, gate, and roll out prompts like code · langfuse.com
- CI/CD for LLM Prompts: How to Build a Prompt Deployment Pipeline · agenta.ai
- What Is Prompt Management for LLMs? · jfrog.com
- Langfuse documentation · langfuse.com
- DeepEval documentation · docs.confident-ai.com
- Ragas documentation · docs.ragas.io
- MLflow documentation · mlflow.org
- GitHub Actions documentation · docs.github.com
- LLMOps Best Practices: From Prototype to Production · llmops.report
- langfuse-docs/content/resources/engineering/llm-regression-testing.mdx at main · langfuse/langfuse-docs · github.com
- Section 62.2: LLMOps & Continuous Improvement · llmbook.icsgen-ai.org