Anthropic Quotes: A New Feature to Improve AI Accuracy | Llm meaning machine learning | Large language models - wikipedia | Large language models tutorial python pdf | Turtles AI

Anthropic’s new Citations feature enables Claude Family AI models to anchor the answers provided to precise document sources, improving reliability in applications such as document summarization and customer service.

Key Points:

• Citations allows Claude model answers to be linked to original content.
• The feature is available on Claude 3.5 Sonnet and Haiku.
• Implemented in the Anthropic API and on Google Vertex AI.
• Costs vary depending on the size and number of documents used.

On Thursday Anthropic introduced Citations, a new feature designed to improve the reliability of Claude AI models. Available in the Developer API and on Google’s Vertex AI platform, Citations allows models to support their responses with specific references to user-uploaded source documents. This innovation, designed to address the problem of AI “hallucinations,” aims to make interactions more precise in areas such as document summarization, question and answer (Q&A) and customer service. Developers can provide models with a document base that is used to generate answers accompanied by citations related to the exact phrases or passages consulted.

Access to this functionality, however, is currently limited to the Claude 3.5 Sonnet and Claude 3.5 Haiku models. Both support Quotations, but with additional costs calculated based on the length and quantity of files uploaded. For example, processing a document of about 100 pages costs approximately $0.30 if Claude 3.5 Sonnet is used and $0.08 with Claude 3.5 Haiku, according to Anthropic’s standard pricing plan. Although this represents an investment, this additional expense may prove justified for developers seeking to dramatically reduce errors and inaccurate interpretations in AI models.

Anthropic emphasizes that integrating Citations into applications not only strengthens accuracy, but also provides end users with greater transparency into the sources of generated answers.

This capability could revolutionize areas where information accuracy is critical, helping to build confidence in the use of AI-based technologies.