Research Assistant

Problem

Researchers spend 3-5 hours filtering through sources, cross-referencing claims, and organizing conclusions for a single research question. Manual synthesis is error-prone, sources get lost, and findings are hard to reproduce.

Solution

Build an agent with ingestion rules for source quality, evidence-first summarization with inline citations, confidence scoring per claim, and an auditable reference trail.

Implementation Steps

1. Set source quality rules

   Define trusted domains, publication date freshness requirements, and source type preferences (primary vs secondary).

   Tip: Weight primary sources 2x higher than secondary. Require at least 2 independent sources per key claim.

2. Build web and document ingestion

   Fetch content from web search, uploaded PDFs, and internal knowledge bases. Normalize into structured snippets with metadata.

   Tip: Define a measurable success metric and review weekly to improve quality and cost.

   # Source ingestion with metadata
   sources = await gather_sources(query, max_results=20)
   for source in sources:
       snippets = extract_key_passages(source.content)
       store_with_metadata(snippets, url=source.url, date=source.published)

3. Deduplicate and rank evidence

   Remove duplicate claims across sources, rank by relevance and source authority, and flag contradictory findings.

4. Synthesize cited conclusions

   Generate concise findings with inline citations linking back to specific source passages. Include confidence levels per conclusion.

5. Create audit trail

   Store all source URLs, extracted snippets, and confidence scores in a structured format for reproducibility and fact-checking.

Recommended combos

FAQs