Common Context grounding patterns

The core components of Context grounding are designed to provide a mechanism that supports finding pertinent information within and across documents, and surfacing only the most relevant pieces needed for a high-quality, low-latency generation from an LLM.

How it works

Searching within documents

The Context grounding service helps you find specific information within a single document more effectively. Instead of just matching keywords, it understands the meaning and context of your search query. For example, if you're looking for information about "apple pie recipes" in a cookbook, it would understand that you're interested in desserts and baking, not technology or fruit farming.

Searching across documents

Context grounding also helps when you need to find information spread across multiple documents. It can understand the relationships between different pieces of information and provide more relevant results. For example, if you're researching "climate change effects on agriculture" across various scientific papers, it would pull together relevant information from multiple sources, understanding that topics like rainfall patterns, crop yields, and temperature changes are all related to your query.

This means you can use Context grounding for:

• Data extraction and comparison: Context grounding can automatically identify and extract specific types of information from documents, then compare them in meaningful ways. Imagine you have a stack of résumés and want to compare candidates' work experiences. The service could extract job titles, durations, and responsibilities, then present them in a way that makes comparison easy, even if the information is formatted differently in each résumé.

• Summarization: Context grounding can create summaries of long documents or multiple related documents. It doesn't pick out random sentences, but understands the key points and overall message. For example, if you have a long report on market trends, the service can provide a summary highlighting the main findings, key statistics, and overall conclusions.