OpenGov summary: Difference between revisions

* - This is the "creator" or "drafter" model.

* - It starts by reading the retrieved official content (e.g., text from a Federal Register notice, an agency program page, or USAspending data).

* - Using retrieval-augmented generation (RAG) techniques, it synthesizes that information into a draft:

**   - Fills in the structured **Cargo template fields** (e.g., program name, sponsoring agency, authorizing legislation, funding amount).

**   - Writes a concise narrative summary for the MediaWiki page.

**   - Proposes relationships (e.g., "This program links to Statute X and Agency Y").

* - Its job is to be creative and comprehensive—turning raw source material into coherent, usable wiki content and graph data—while staying grounded in what was retrieved.

* - This is the "checker" or "fact-checker" model.

* - It runs **independently** after the generator finishes its draft.

* - It goes through every part of the draft step-by-step:

**   - Compares each claim, field value, and relationship directly against the original source documents.

**   - Scores for factual accuracy (e.g., does the funding number match exactly?).

**   - Checks citation completeness (is every key fact traceable?).

**   - Evaluates logical consistency and neutrality (no unsupported assumptions or biased phrasing).

* - It gives an overall confidence score and flags any mismatches, gaps, or potential issues.

* - If both AIs agree at a high threshold (≥95% confidence), the draft auto-publishes as a new page revision.

* - If there's disagreement or low confidence, the item flags for quick human review (one-click approve/reject/retry on the Clearance Dashboard).

* - A single AI can sometimes confidently produce wrong or invented details (a common issue in LLMs).

* - By having one model **create** and a different model **critically review**, the system catches more errors—studies on multi-agent or dual-LLM verification show significant reductions in hallucinations (often 60-90% in similar pipelines).

* - Alternating roles (e.g., Grok drafts one time, Gemini verifies; next time they swap) adds extra robustness by avoiding patterns from one model's weaknesses.

* - In OpenGov Encyclopedia, this keeps the process fast and mostly automated (~80-95% hands-off) while meeting federal needs for defensibility, traceability, and neutrality.

In short:  

* - **Generator** → Builds the draft from official sources.  

* - **Verifier** → Double-checks it rigorously before anything goes live.