Add Source Discovery to an AI Research Workflow

Описание и применение

Внедрение структурированного процесса поиска, извлечения и проверки источников повышает достоверность цитат в ответах ИИ.

Задача

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

Результат

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

Рабочий процесс

• 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• Review each candidate’s observed text and decide if it is useful, irrelevant, inaccessible, or outdated.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.

Доказательства

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.