> For the complete documentation index, see [llms.txt](https://docs.raga.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raga.ai/ragaai-prism/test-inventory/large-language-model-llm.md). # Large Language Model (LLM) Large Language Models (LLMs) are developed to understand and generate human-like text by predicting the next word or phrase in a given context. These models are especially adept at tasks like text completion, question answering, summarisation, and translation. RagaAI, as a testing platform for AI applications, evaluates the model's ability to comprehend, respond, and maintain coherence across diverse contexts. By providing detailed analytics on areas where the model underperforms or exhibits unexpected behaviours, RagaAI enables Data Scientists to pinpoint specific weaknesses. The insights gained from RagaAI's comprehensive testing help in advancing the overall performance and trustworthiness of Language Models --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.raga.ai/ragaai-prism/test-inventory/large-language-model-llm.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.