> ## Documentation Index > Fetch the complete documentation index at: https://docs.literalai.com/llms.txt > Use this file to discover all available pages before exploring further. # Evaluation > Learn how to evaluate your LLM applications and agents. ## Why evaluate? Evaluation is key to enable continuous deployment of LLM-based applications and guarantee that newer versions perform better than previous ones. To best capture the **user experience** one must understand the multiple steps which make up the application. As AI applications grow in complexity, they tend to chain multiple steps. Literal AI lets you log & monitor the various steps of your LLM application. By doing so, you can continuously improve the performance of your LLM system, building the most relevant metrics: | Level | Metrics | | ------------------- | --------------------------------------------- | | LLM Generation | Hallucination, Toxicity, etc. | | Agent Run | Task completion, Number of intermediate steps | | Conversation Thread | User satisfaction | An example is the vanilla **Retrieval Augmented Generation** (RAG), which augments Large Language Models (LLMs) with domain-specific data. Examples of metrics you can score against are: context relevancy, faithfulness, answer relevancy, etc. ## How to think about evaluation? Scores are a crucial part of developing and improving your LLM application or agent. | Who? | When? | Type of eval metrics | Example | | -------------- | ---------------- | ------------------------------------------------------------- | ----------------------------------------------------------------------------------- | | End-User | In Production | Explicit Feedback (👍👎) | Thumbs-up or down on a chatbot's answer | | End-User | In Production | Implicit Feedback based on product metric | User conversion to paid offering increases by 15% | | LLM-as-a-Judge | In Production | AI evaluation (without ground truth) | Hallucination, context relevancy, etc. | | LLM-as-a-Judge | During Iteration | AI evaluation against a Dataset (with ground truth or not) | Hallucination, conciseness, helpfulness, context relevancy, answer similarity, etc. | | Domain Expert | During Iteration | Human evaluation against a Dataset (with ground truth or not) | Hallucination, conciseness, helpfulness, context relevancy, answer similarity, etc. | # Leverage Literal AI Automatically evaluate your LLM logs in production, monitor performance and detect issues. Add scores to your LLM logs using the SDKs.