docs: sync D14 with best-of-3 confirmation and fix stale counts

README.md

@@ -81,10 +81,10 @@ small enough that a framework would be cost without benefit, not "doesn't
 know how to use one."
 
 **AI / evaluation:** Anthropic Claude (tool use / structured output for the
-paid baseline generator + research-assist drafting) · Groq/Llama (free-tier
-alternative for the same baseline generator, the `LLMJudge`, and
-research-assist's default model, so nothing in this project *requires* a
-paid key) · a custom evals harness design (leakage / grounding / richness)
+paid baseline generator) · Groq/Llama (free-tier alternative for the same
+baseline generator, the `LLMJudge`, and the only model `research_assist.py`
+uses — no Anthropic path exists there, so nothing in this project *requires*
+a paid key) · a custom evals harness design (leakage / grounding / richness)
 with a calibrated judge, verified against planted leaks and real human
 spoiler reviews.
 

docs/DESIGN.md

@@ -150,7 +150,7 @@ is right. Instead, TMDB is a **third tier**, structurally kept apart:
 | Tier | Source | Size | Has a curtain / cited claims? |
 |---|---|---|---|
 | Measurement | `evals/dataset/titles.yaml` | 20, fixed | N/A — it's the experiment |
-| Researched | `content/researched/*.json` | 7, hand-written | Yes |
+| Researched | `content/researched/*.json` | 8, hand-written | Yes |
 | **Browse (TMDB)** | `src/preshow/tmdb.py` | effectively all of TMDB | No — poster + synopsis only |
 
 Concretely:
@@ -353,10 +353,28 @@ three prompt iterations**: a single generation call from a small free model is
 to 15 grounded claims run to run, with no temperature pinned. Two concrete prompt bugs were
 found and fixed this way (a fabricated-looking "score" entry with no real source behind it;
 `questions` and `debate_prompts` coming back as literal duplicates) — both confirmed fixed
-by direct comparison of successive drafts. What's still open, not yet built: run-to-run
-depth/quality variance is real and unresolved. The likely fix is generating 2-3 drafts per
-title and picking the most complete one programmatically (e.g. by grounded-claim count),
-rather than trusting a single call — not implemented yet, left for a follow-up session.
+by direct comparison of successive drafts.
+
+**Fix implemented and confirmed working live.** `draft_best_of()` generates 3 independent
+candidates per title from the same shared retrieval (`temperature=0.8` for real diversity),
+sanitizes each candidate's citations first (so a candidate can't win by fabricating extra
+ones), and keeps the one with the most grounded claims; `main()` now defaults to this
+instead of a single `draft()` call. First committed without a live end-to-end run — Groq's
+API returned a network-level 403 ("Access denied, check your network settings") for every
+request from that session's execution environment specifically (a bare `curl` to
+`/v1/models` failed the same way; the user confirmed it worked from their own
+browser/terminal, with or without their VPN, the whole time). The 403 resolved on its own
+in a later session with no code change and no identified root cause (possibly a temporary
+Cloudflare IP flag) — `python webapp/research_assist.py "Citizen Kane" 1941 3` then ran
+end-to-end for real: all 3 candidates succeeded (4, 4, 5 grounded claims), the 5-claim one
+was correctly selected, zero fabricated citations reached the output.
+
+One real, minor quality issue surfaced by that live run, left as a future prompt fix, not a
+grounding violation: `author_voice` came back as the model's own generic critical
+commentary ("As a film-literate critic, I approach...") rather than an actual quote or
+statement from someone who made the film (director, screenwriter) — the field's intended
+content, as seen in Gone Girl's Fincher/Flynn quotes. It still cites the retrieved
+Wikipedia source correctly; it's a category mismatch, not a fabrication.
 
 ## Rejected