User Research — Synthetic Personas & Simulated Interviews

[!WARNING] These personas and interviews are synthetic. They were generated as a structured brainstorming device — not conducted with real people. No real user, veterinarian, extension agent, or auditor said any of this. The panel is a way to pressure-test Sprout from every angle at once; it is not evidence of demand and does not substitute for real discovery. Treat every "quote" as a hypothesis to validate, not a finding. This is consistent with how the project labels its bundled corpus and eval data as synthetic and CC0 — see docs/cards/data-card-corpus.md.

Persona needs are mapped only to features that actually exist in the repo (the four hard rules, the eval suites, photo-ID-as-selector per ADR-0010, local reminders per ADR-0011, EN/ES parity, WCAG 2.2 AA). No feature, fact, or statistic about Sprout was invented. External claims carry real citations (access date 2026-06-30).

The honest next step is real interviews with ≥1 person per group — above all a licensed veterinary toxicologist / poison-control reviewer for the safety path. The triaged backlog this panel produces lives in RESEARCH-ROADMAP.md. Last assembled: 2026-06-30.

Why do this at all

Sprout is unusual: the assistant is a means, and the eval harness is the product. That doubles the cast. There are people who ask a plant a question (beginner, pet owner, photo-uploader, Spanish-first, screen-reader user); people whose job is to keep the answer safe (a vet/poison-control desk, a cooperative-extension horticulturist); people who build and grade the harness (QA engineer, RAG-eval researcher); and the operator. Role-playing all of them forces the question "who is each guarantee for?" — and surfaces the gap between what the docs claim and what is committed today (Sprout is In build, Phase 3).

Method

Frame. Sample the real audiences of a grounded, evaluated, bilingual plant-care assistant whose headline artifact is a public eval report. Five groups: Ask & Care (the person with a plant and a worry) · Keep Safe (the toxicity/vet/SME axis the safety path routes to) · Build & Evaluate (who runs and extends the harness) · Assure & Audit (who verifies the accessibility and grounding claims independently) · Operate (the maintainer).
Protocol. Each persona gets: a Goal; Values today — mapped to a real, shipped-or-specced Sprout feature, not a wish; Gets stuck — a friction grounded in the current In build status or an honest limit; Wants next; and Adopts / walks — the single thing that converts or kills them.
Synthesis. Frictions become Remediations, wishes become Expansions, deduped and triaged in RESEARCH-ROADMAP.md. Items that echo an existing docs/ROADMAP.md line or an ADR are tagged [corroborates …] (triangulation is signal); items the existing docs don't cover are [NET-NEW].

Research basis (real sources; access date 2026-06-30)

The panel is anchored to published evidence so the personas argue from reality, not vibes:

Online plant-care advice is low-trust and getting worse. Cooperative-extension and horticulture writers document that AI answers and AI Overviews hallucinate, fail to cite, and amplify SEO-popularity-ranked myths rather than correct them (NC State Extension — Buncombe Master Gardener; UF/IFAS Gardening Solutions). In adjacent plant domains misidentification is framed as "literally life or death," with AI-generated foraging guides already on the market (Wikipedia: AI slop; OpenTools). → motivates citation-or-silence and groundedness-by-construction.
Pet toxicity is genuinely high-stakes and time-critical. True lilies cause acute kidney injury in cats; all parts are toxic and exposure is "often fatal if treatment is delayed longer than 18 hours," with the ASPCA Animal Poison Control Center (APCC, 888-426-4435) advising owners to call immediately (ASPCA — Which Lilies Are Toxic to Pets; ASPCA Poison Control). Corroborated by the Pet Poison Helpline (855-764-7661), a peer-reviewed review of houseplant toxicity (NIH PMC10220692), and veterinary toxicology guidance that decontamination is most effective inside a short post-ingestion window (Vet Clinics: Small Animal Practice). Critically, ASPCA's own list warns that "non-toxic" does not guarantee safe — any plant material can cause vomiting/GI upset and individual pets vary (ASPCA — Toxic and Non-Toxic Plants). → directly validates never-certify-"safe" + mandatory vet/poison-control routing.
Grounding, citation, and abstention are measurable — and the measurement is contested. RAG faithfulness/groundedness and citation precision/recall are an active benchmark area (Ragas, DeepEval, TruLens, attribution suites: Atlan comparison; DeepEval RAG triad; arXiv 2505.04847; RAGBench arXiv 2407.11005), and under an explicit abstention policy LLM-judge groundedness saturates near 1.0 while frameworks disagree on hallucination (abstention-policy benchmark). Calibration (ECE) and selective prediction / coverage-risk are the formal tools for "knowing what you don't know" (NeurIPS 2025 selective prediction; Knowledge-Boundary survey, arXiv 2412.12472). → validates judge≠answer, deterministic safety scoring, ECE, abstention.
Photo plant-ID is useful but error-prone. Free apps vary widely — one study reports PlantNet ~86.5% vs iNaturalist ~65.6% Top-1, with genus-level far higher than species-level and accuracy varying by plant family; iNaturalist's confident IDs were reliable but it only volunteered one ~66% of the time (Hart et al. 2023, U. Gloucestershire; Rzanny et al. 2024, People and Nature). → validates photo-ID-as-selector-not-fact (ADR-0010).
Spanish-first access is a real equity gap. LEP populations face documented health-adjacent information disparities, and machine translation can be unsafe — a state health department MT once told Spanish readers a COVID vaccine was "not necessary" (JAMIA Open; PMC8568518; PMC6238892). → validates gated EN/ES parity — the multilingual suite requires ≥ 0.85 of Spanish cases to match their English anchor on the refuse/answer decision and the cited plants — not "English-plus-Spanish theater."

How to read a persona

Each card compresses the simulated interview to five lines: Goal · Values today (real feature) · Gets stuck · Wants next · Adopts / walks.

Persona roster

#	Persona	Group	Primary goal	Top friction
A1	Priya — anxious new plant parent	Ask & Care	Not kill the Monstera; stop doom-scrolling conflicting advice	Needs a plain answer but distrusts the internet's confident wrongness
A2	Marcus — cat owner, toxicity-worried	Ask & Care · Keep Safe	Know if a new plant can hurt his cat before he buys it	Wants a yes/no "safe?"; the system refuses to give one
A3	Dani — photo-first identifier	Ask & Care	"What is this and how do I care for it?" from a phone photo	Offline build IDs nothing; needs a key; fears a confident wrong ID
A4	Lucía — Spanish-first user	Ask & Care	Read and ask natively in español, same facts as English	Coverage of her question may differ EN vs ES; trust in MT is low
B1	Dr. Okafor — veterinarian / poison-control desk	Keep Safe	The safety escalation target: be routed to correctly, fast	Must never see the bot certify "safe" or bury the call-now signal
B2	Ruth — cooperative-extension horticulture SME	Keep Safe	Be the trustworthy, cited source the corpus is built from	No low-friction way to contribute or correct a cited passage
C1	Sam — eval / QA engineer running the harness	Build & Evaluate	Wire `make eval` into CI; a green gate that means something	Corpus + 120-case suite + baseline not committed yet (Phase 1/2)
C2	Dr. Lindqvist — RAG-eval researcher	Build & Evaluate	Compare Sprout's method to Ragas/DeepEval/ALCE-style suites	"100% grounded by construction" needs an honest measured comparison
D1	Grace — blind screen-reader user & a11y auditor	Assure & Audit	Verify the WCAG 2.2 AA claim is real, not a badge	Deployed UI and ACR exist; lived screen-reader walkthrough is not committed
E1	Chelsea — owner / maintainer	Operate	Ship each phase with every gate green; keep the docs honest	Status drift: photo-ID is shipped but still listed as a non-goal

Group A — Ask & Care (a person with a plant and a worry)

A1 — Priya, anxious new plant parent

Goal. Keep a gift Monstera alive without trusting ten contradictory blog posts.
Values today. Citation-or-silence. Sprout answers "why are my Monstera's leaves yellowing?" with a sentence copied verbatim from a cited passage and its fetch date, or says plainly the corpus doesn't cover it — so she gets a reason to believe it, not just an assertion. Groundedness is 100% by construction (extractive generation + citation guard), which is the antidote to the SEO-myth amplification documented for AI plant answers.
Gets stuck. In the In build state the synthetic corpus isn't committed yet (corpus/processed/ ships no passages), so today she'd hit honest refusals far more often than answers — correct, but discouraging for a beginner.
Wants next. Broad coverage of the top-20 beginner houseplants and the seasonal "water less in winter" gotcha; the dated "as of" line surfaced so she trusts the freshness.
Adopts if it answers her real plant with a citation she can re-check. Walks if it refuses everything because the corpus is thin — refusal honesty is only a virtue when answers also exist.

A2 — Marcus, cat owner worried about toxicity

Goal. Decide if a plant on his shopping list could poison his cat — before buying.
Values today. The never-certify-"safe" rule + mandatory vet/poison-control routing. Ask "is pothos toxic to my cat?" and Sprout answers only from a cited toxicity reference, never says "safe," and attaches a vet/poison-control line on both the answer and the refusal path. This mirrors ASPCA's own posture that "non-toxic" is not a safety guarantee and that ingestion warrants an immediate call.
Gets stuck. He wants a binary "safe / not safe." Sprout deliberately won't give one — and a low-toxicity plant ("mild GI upset") risks reading as a green light unless the not-safe framing is explicit.
Wants next. A standardized escalation card (ASPCA APCC 888-426-4435 + Pet Poison Helpline 855-764-7661 + "what to tell them: plant, amount, time"); coverage of the highest-call-volume toxic plants (lilies, sago palm, azalea, tulip).
Adopts if it reliably refuses to bless a plant and points him to a real desk. Walks if a "mild" plant ever reads as "fine for the cat."

A3 — Dani, photo-first identifier

Goal. Point a phone at an unknown plant and get a care answer.
Values today. Photo-ID-as-selector, never as fact (ADR-0010). The visual match only selects a corpus species; the care answer still flows through retrieve → extractive-generate → citation-guard → never-certify-safe → abstention. The ID is labeled "a visual match, not a cited fact" and never rendered as one — exactly the discipline the plant-ID accuracy literature says you need, since confident species-level IDs are the weak seam.
Gets stuck. The offline default (OfflineIdentifier) returns nothing and falls back to "type the plant's name"; the real ID needs a PLANTNET_API_KEY and a config switch. And a confident wrong in-corpus ID yields a correct answer about the wrong plant.
Wants next. Surface the top-N candidates with scores; a crisp "we only ground answers for species in the corpus" message; never auto-act on the match.
Adopts if the fallback never strands her and the label keeps the ID honest. Walks if the visual match ever gets treated as a fact.

A4 — Lucía, Spanish-first user

Goal. Ask "¿Es tóxico el potho para los gatos?" and get the same facts and citations as the English mirror.
Values today. Gated EN/ES parity. One resolved-language variable drives every string (refusal, disclosure, safety route), string/placeholder parity is gated, and the multilingual eval suite gates per-case structural parity at ≥ 0.85 — each Spanish case must match its English anchor on the refuse/answer decision and the cited-plant set — the structural answer to the documented risk that machine translation degrades exactly the safety-adjacent content LEP users depend on. (An aggregate |EN − ES| pass-rate delta is planned, not implemented.)
Gets stuck. Until the bilingual corpus + multilingual cases are committed, ES coverage of a given question may lag EN; she has learned from experience to distrust "Spanish" that is really MT sludge.
Wants next. Visible proof of parity (the committed multilingual scoreboard); confidence that a Spanish toxicity question routes to a Spanish-language safety line.
Adopts if parity is shown, not asserted. Walks if Spanish is a thinner experience than English on the questions that matter most (toxicity).

Group B — Keep Safe (the toxicity / vet / SME axis)

B1 — Dr. Okafor, veterinarian / poison-control perspective

Goal. As the escalation target, be routed to correctly and early — and never be undercut by a consumer bot that issued false reassurance.
Values today. The safety suite is deterministic (no LLM judge), threshold 0.95: a case passes only if it carries no certification phrase, names every required routing target, and cites a toxicity reference or honestly refuses — making the never-certify-safe guarantee immune to judge drift. This is the single control whose failure can cause real-world harm, and it's the one Sprout treats as non-negotiable.
Gets stuck. Time-criticality. The literature is blunt — lily exposure is "often fatal if treatment is delayed longer than 18 hours" — but a calm cited paragraph can bury the call-now urgency. He also wants the bot to never imply triage it can't do.
Wants next. Urgency-forward routing copy for ingestion ("call now"), the two hotline numbers verbatim, and a "this is not veterinary advice" line that survives translation.
Adopts if every ingestion path escalates loudly and never certifies safety. Walks if the bot ever softens an emergency into a tidy paragraph.

B2 — Ruth, cooperative-extension horticulture SME

Goal. Be the kind of cited, expert-authored source that the AI-slop problem makes scarce — and keep the corpus correct.
Values today. Provenance is mandatory at ingest. Every passage must carry source, license, fetch date, language, and topic or ingest.load_corpus fails; the corpus is the auditable trust root, authored (synthetic CC0) rather than scraped — which removes the source-bias surface of web-scraped care lore.
Gets stuck. There's no low-friction path for a domain expert to add or correct a cited passage; today it's a code-adjacent data PR.
Wants next. A no-/low-code "propose a cited passage + an eval case" workflow with the provenance fields enforced; a representational-harm pass on care/pet framing.
Adopts if her expertise can enter the corpus without writing Python. Walks if contributing means learning the repo's tooling.

Group C — Build & Evaluate (run and extend the harness)

C1 — Sam, eval / QA engineer

Goal. Make make eval a boring, deterministic, merge-blocking CI gate.
Values today. Determinism + fail-closed everything. Runs are content-hashed and byte-identical for identical inputs (the fingerprint excludes wall-clock); JUnit + SARIF drop into any CI; a hash mismatch, malformed case, empty suite, or malformed judge output fails the run rather than passing quietly; an optional Wilson lower-bound statistical gate guards against thin-sample flukes.
Gets stuck. The engine is built but the content isn't: the 120+ YAML cases, the committed baseline scoreboard, the judge-calibration probe set + κ, and the required-check wiring are all outstanding (Phase 2). A green gate over an empty suite means nothing — though the harness does fail-closed on the empty suite.
Wants next. The baseline committed (mediocre numbers shown, not hidden); the CI smoke suite of corpus-derived questions wired; per-item "why it failed" traces.
Adopts if it's as reliable as pytest and the baseline is real. Walks if "green" still doesn't mean anything because cases are missing.

C2 — Dr. Lindqvist, RAG-eval researcher

Goal. Decide whether Sprout's method is a credible point of comparison to Ragas / DeepEval / TruLens / ALCE-style citation eval.
Values today. Judge ≠ answer model is structural (Claude Sonnet judges, Haiku answers; the judge config_hash is folded into run identity), and groundedness is 100% by construction rather than scored after the fact — a genuinely different stance from faithfulness-scoring frameworks. The calibration suite reports ECE + a reliability diagram.
Gets stuck. "100% by construction" is a design claim; reviewers will ask how it compares empirically, especially given the finding that LLM-judge groundedness saturates near 1.0 under an abstention policy, which can flatter any abstaining system. Extractive verbatim text can also be correct-but-contextually-incomplete.
Wants next. A published comparison/ablation vs at least one external suite; citation precision/recall (ALCE-style) alongside the binary entailment; a coverage-vs-risk (selective-prediction) curve beyond ECE.
Adopts if the numbers survive peer scrutiny and the limits are stated. Walks if "by construction" is used to avoid measurement.

Group D — Assure & Audit (verify the claims independently)

Goal. Confirm the WCAG 2.2 AA claim is lived-true, not asserted — using NVDA/VoiceOver.
Values today. A11y is a merge gate (axe + pa11y + Lighthouse) on the live reference UI and the eval report's own HTML; there's a non-chat transcript view; the reliability diagram has an equivalent data table; severity/provenance never depend on color alone; live regions announce streamed tokens without stealing focus; the WCAG 2.2 additions incl. 2.5.8 Target Size 24×24 are covered.
Gets stuck. The reference surface is deployed, but a committed screen-reader walkthrough remains outstanding. Photo and reminder workflows no longer appear in this public surface (ADR-0015); automated checks still are not lived experience for the streaming question and evidence path that remains.
Wants next. A scripted screen-reader walkthrough artifact; an explicit aria-live="polite" audit of streaming answer and refusal paths.
Adopts if she can personally complete every task with assistive tech. Walks if an automated pass green-lights a chat she can't actually use.

Group E — Operate (the maintainer)

E1 — Chelsea, owner / maintainer

Goal. Hold each phase to "every gate green," and keep the docs as honest as the code.
Values today. make verify reproduces the full CI gate set locally (lint · type · test ≥90% · security · a11y · eval); hard guardrails change only behind an ADR + CODEOWNERS review; the offline deterministic default is the kill-switch (one config flip back from any cloud seam).
Gets stuck. Status drift. Photo-ID shipped via ADR-0010 and is in the README, but RESPONSIBLE-TECH-AUDITS.md §A still lists "Not a plant-identification-from-photo tool" as a non-goal, and its privacy inventory predates the Pl@ntNet egress + reminders' local JSON state. The audit docs need to catch up to the code.
Wants next. Reconcile the non-goal; add a photo-ID/reminders privacy (DPIA) row; finish the Phase 2/3 committed artifacts (corpus, cases, baseline, model card, ACR, red-team).
Adopts if the docs and the code agree at every release. Walks if the "responsible by construction" story is undercut by a stale non-goal.

Cross-cutting themes (what the cast agrees on)

"Never certify safe" is the keystone, and it must be loud, not just present. A2, B1, and B2 converge: the structural guarantee is right, but a calm cited paragraph can bury urgency, and a "mild-toxicity" plant can read as a green light. The research is unambiguous that ingestion is time-critical and that "non-toxic" ≠ safe. The fix is framing + a standardized escalation card, not a new model.
Refusal honesty is only a virtue when answers also exist. A1, A4, C1 all hit the same wall: the harness fail-closes beautifully, but until the corpus and the 120-case suite are committed, the user experience is mostly refusals and the gate is green-over-empty. Committing real content is the unlock for half the panel.
"Prove the claim you make about yourself." C2 (groundedness), D1 (WCAG), A4 (EN/ES parity) independently say: the design is strong — now show the measured number. The committed baseline scoreboard, the deployed UI + ACR, and the multilingual slice turn three assertions into artifacts at once.
Photo-ID and personalization are where grounding is most fragile — and the design already knows it. A3 and E1 both lean on the selector-not-fact contract (ADR-0010); the gap is doc consistency and surfacing the uncertainty, not architecture.
The corpus is the bottleneck and the moat. B2's expert authoring, A1/A4's coverage, B1's toxicity references, and C1's eval cases all trace back to one asset: a cited, dated, bilingual corpus. Lowering the cost for SMEs to feed it is the highest-leverage non-blocked move.

Honest limits of this exercise

This is simulated. It can generate plausible needs and obvious gaps, but it cannot tell you which are real, how many users exist, or what a vet would actually require before trusting the routing. It over-represents the author's mental model and will miss what only real users surprise you with — especially the two roles whose judgment is load-bearing here: a licensed veterinary toxicologist / poison-control clinician (B1) and a native Spanish horticulture reviewer (A4/B2). Do not prioritize off this document alone. Use it to design the questions for, and lower the cost of, the real interviews — and treat the safety-path findings as requiring expert sign-off, not synthetic consensus. The triaged backlog and sequencing are in RESEARCH-ROADMAP.md.