How does Claude differentiate between context and noise in long documents

The Claude API from Anthropic is designed to handle long, complex documents by leveraging its extended 200,000 token context window. To help Claude differentiate between relevant context and noise in these large inputs, Anthropic recommends several prompt engineering techniques:

1. Place the longform data, such as documents or reports, at the top of the prompt, above the specific query or instructions. This ensures Claude has the full context before attempting to answer. [1][3]

2. Structure the document content and metadata using XML tags like ``, ``, and ``. This provides clarity on the different sources and types of information Claude is working with. [3]

3. Instruct Claude to first quote the most relevant parts of the documents before providing its final answer. This helps it focus on the pertinent information rather than getting distracted by extraneous details. [3]

4. Include a few example questions and answers about other sections of the document. This "few-shot" approach can guide Claude to better understand the type of information that is most relevant. [1]

5. Position the specific query or instructions at the end of the prompt, as this can improve Claude's recall of the key task it needs to perform. [1]

By structuring prompts in this way, developers can help Claude parse long, complex documents, identify the most salient information, and deliver focused, context-aware responses. The extended context window gives Claude powerful capabilities, but effective prompt engineering is crucial to get the most out of this feature.