Add Source Discovery to an AI Research Workflow

Description and use

Implementing a structured process for discovery, retrieval, and evidence review improves citation reliability in AI-generated answers.

Problem

AI research answers often cite URLs without verifying that the linked pages support the claims, leading to unsubstantiated citations.

Outcome

The workflow introduces explicit stages—discovery, retrieval, review—allowing AI agents to generate answers with verifiable citations, reducing misinformation.

Workflow

• Define a discovery contract that records query, context, and candidate metadata.
• Use the Scrapeless Google Search API to fetch search results as JSON captures.
• Save the raw capture and preserve HTTP status and task ID for pending requests.
• Run a local adapter (source_candidates.py) to transform captures into candidate records with state (pending, transport_error, http_error, unmapped, empty).
• Review each candidate’s observed text and decide if it is useful, irrelevant, inaccessible, or outdated.
• Retrieve full pages for selected candidates and extract evidence text.
• Feed reviewed evidence into the answer generation stage, ensuring citations link back to verified content.
• Iterate searches as needed while maintaining context and provenance.

Evidence

Search access does not automatically create that missing connection.

The adapter gives the next reviewer enough information to understand where a candidate came from.

This article does not report a live API request or a page-retrieval run.