Package the evidence
Preserve the source URL, title, retrieval time, and selected passage in a stable record.
Search Engine LLM / Ground
Give your LLM context.
Keep the source attached.
Turn search results into small, inspectable evidence packets for a large language model—without treating retrieved text as instructions.
Grounding is a workflow, not a guarantee.
Define the task and appropriate source scope, select relevant passages where access and reuse are permitted, and keep source identifiers attached throughout generation and display. Validate that citations refer to retrieved material and that each passage actually supports the associated claim.
Protect the boundary
Keep external text in the role of data, limit tool authority, and exclude unnecessary secrets.
Test insufficient evidence
Include unanswerable and conflicting-source cases before expanding the collection.
The JSON structure is illustrative and provider-neutral. Live retrieval and generation require a separately configured application.
KEEP EXPLORING
Connect the next part of your workflow.
Connect search to an LLM without losing the source
Build evidence packets, select relevant passages, validate citations, and test what happens when the sources cannot answer.
Read the full guideAI Search Engine
Explore AI search as three distinct jobs: discovering sources, retrieving relevant evidence, and generating an answer within that evidence.
Explore the topicSearch Engine API
Explore how a search engine API turns discovery into structured data—and choose the source that fits the question your application needs to answer.
Explore the topicYOUR NEXT IDEA STARTS HERE
A little search data.
A whole lot of possibility.
Explore the patterns, understand the tradeoffs, and build from a clearer starting point.