Offline evaluation
Offline evaluation involves assessing the agent’s performance under controlled conditions before deployment. This includes benchmarking to establish general performance baselines and more targeted testing based on generated test cases. Offline evaluations provide key metrics, error analyses, and pass/fail summaries from controlled test scenarios, establishing baseline performance.
While human assessments are sometimes seen as the gold standard, they are hard to scale and require careful design to avoid bias from subjective preferences or authoritative tones. Benchmarking involves comparing the performance of LLMs against standardized tests or tasks. This helps identify the strengths and weaknesses of the models and guides further development and improvement.
In the next section, we’ll discuss creating an effective evaluation dataset within the context of RAG system evaluation.
Evaluating RAG systems
The dimensions of RAG evaluation discussed...
