Why evaluation matters
Large language model apps rely on many interacting components. Understanding their behavior is essential before scaling.
Core building blocks
LLM models, prompts, context sources, memory, tools, control flow and guardrails all influence output quality.
Unique testing challenges
Randomness, subjectivity, latency and broad scopes make unit style tests tricky. Integration tests and end-to-end checks are usually better choices.
Judgment types
Binary, categorical, ranking, numerical and text judgments each suit different goals. Simple types are easier to source reliably.123
Sourcing judgments
Heuristic code, other models and human reviewers all provide feedback. Recent work shows well prompted models can match human ratings.456
Evaluation stages
- Interactive: small scenario playgrounds for quick feedback
- Batch offline: curated benchmarks in continuous integration
- Monitoring online: collect real usage data and alerts
Building datasets
Use public benchmarks like Chatbot Arena or MMLU for baselines.78 Gather real user interactions and synthesize new examples with LLMs.
Future directions
Better model based evaluators, multi-agent workflows and end-to-end optimization will expand best practices. Continuous data generation and rigorous evaluation loops are key.