> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/4.1-general-use-cases.md).

# 4.1 General use cases

- [Use cases](https://docs.layerlens.ai/4.1-general-use-cases/general.md): Concrete ways teams use LayerLens — model evaluation, agentic eval, RAG, CI/CD gates, continuous eval.
- [Model evaluation](https://docs.layerlens.ai/4.1-general-use-cases/model-evaluation.md): Pick the right model for a task by running it against the right benchmark.
- [Benchmark-driven development](https://docs.layerlens.ai/4.1-general-use-cases/benchmark-driven-development.md): Make benchmarks a first-class signal in your dev loop — every change runs against a benchmark, every result is on the dashboard.
- [Agentic evaluation](https://docs.layerlens.ai/4.1-general-use-cases/agentic-evaluation.md): Pre- and post-deployment quality gates for multi-step agents — combine assertions and LLM graders.
- [Agentic evaluations — overview](https://docs.layerlens.ai/4.1-general-use-cases/agentic-evals-overview.md): Pre- and post-deployment quality gates for multi-step AI agents — assertions and LLM graders.
- [Continuous evaluation](https://docs.layerlens.ai/4.1-general-use-cases/continuous-evaluation.md): Score live production traces on a recurring schedule — surface drift before customers do.
- [RAG evaluation](https://docs.layerlens.ai/4.1-general-use-cases/rag-evaluation.md): Evaluate RAG pipelines end-to-end — retrieval quality, faithfulness, and answer quality.
- [AI quality gates in CI/CD](https://docs.layerlens.ai/4.1-general-use-cases/ai-quality-gates-cicd.md): Block prompt and model regressions in your merge pipeline — turn evaluations into a hard gate.