Back to Prompt Library
testing

Integrate Coval for RAG Observability

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: LLM-Powered Legal & Market Intelligence

Format
Text-first
Lines
1
Sections
1
Linked challenge
LLM-Powered Legal & Market Intelligence

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines
1 sections
No variables
0 checklist items
Integrate Coval into your LlamaIndex RAG pipeline to monitor and evaluate its performance. Specifically, set up Coval to track query execution, retrieved chunks, and the final LLM response. Explain how you would use Coval's metrics (e.g., context precision, context recall, answer relevance) to identify areas for improvement in your RAG system. Provide pseudo-code or conceptual steps for integrating Coval's logging and evaluation hooks within your LlamaIndex `QueryEngine`.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Preserve the rubric, target behavior, and pass-fail criteria as the baseline for evaluation.

Tune next

Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.

Verify after

Make sure the prompt catches regressions instead of just mirroring the happy-path examples.