The New PM Trinity: Measuring Hallucination, Helpfulness, and Latency in AI Products
Daily active users and conversion rates still matter but they’re no longer sufficient to run modern AI products. What decides whether your AI feature earns trust and compounding engagement is a new trinity of product metrics:
Hallucination (how reliably the system stays grounded in facts),
Helpfulness (how much it actually achieves for the user), and
Latency (how fast it feels and how quickly it delivers value).
This post is a practical, research‑informed playbook for PMs who need to instrument AI features with a balanced scorecard that goes beyond dashboards full of DAU/retention curves. We’ll define the metrics, show how to measure them online and offline, and propose targets and trade‑offs that maintain quality and user trust.
Why a balanced scorecard for AI
Classic Balanced Scorecard thinking asks teams to balance multiple dimensions rather than over‑optimize a single KPI. Kaplan and Norton’s early framing - tracking “the measures that drive performance” across financial, customer, internal process, and learning perspectives - applies cleanly to AI features as well.(Harvard Business Review)
For AI, the “customer” perspective hinges on trust. Two realities make that explicit:
Hallucination is measurable and nontrivial. Benchmarks like TruthfulQA showed early general‑purpose LMs were truthful on just ~58% of questions, compared to ~94% for humans.(arXiv)
Helpfulness is not the same as accuracy. “Helpful, honest, harmless” alignment work (RLHF) improved perceived utility, but helpfulness must be operationalized in your task context (e.g., code merged, ticket resolved, document draft accepted).(arXiv)
Latency shapes satisfaction and conversion. A well‑known study on digital experiences found that shaving 0.1 seconds from mobile load times improved progression rates throughout purchase funnels - small delays have outsized behavioral impact.(web.dev)
Put differently: the AI that people keep using is the one that is fast, useful, and trustworthy under scrutiny.
1) Hallucination: from anecdotes to auditable numbers
Definition (product‑level): The proportion of generated claims not supported by credible sources, or contradicted by ground truth. In practice, define hallucination per atomic fact, not per whole response.
Recommended offline metrics
FActScore – Break generations into atomic facts and compute the percentage that are supported by evidence. This creates a fine‑grained factual precision score (e.g., “78% of claims supported”). The authors report that coarse, binary judgments miss important detail; notably they observed ChatGPT at ~58% factual precision on biography tasks at the time of study.(ACL Anthology)
TruthfulQA score – Stress‑test susceptibility to common misconceptions across domains (health, law, finance, politics). Early results highlighted that simply scaling models is not enough for truthfulness.(arXiv)
QAFactEval (especially for summarization/grounded generation) – A QA‑based factual consistency metric that improved ~14% over prior QA‑based methods on a standard benchmark.(ACL Anthology)
SelfCheckGPT (zero‑resource detection) – Sample multiple answers and flag inconsistent claims as likely hallucinations when external validation isn’t available. Useful for black‑box models.(arXiv)
Recommended online/production metrics
Unsupported Claim Rate (UCR): % of extracted claims in user‑visible answers that lack a linked source (for RAG features) or contradict the retrieved sources. (Automate with FActScore‑style claim extraction plus retrieval checks.)(arXiv)
Citation Coverage: % of answers with at least one verifiable citation; Citation Precision: % of citations that truly support the claims (audit via QAFactEval‑style checks).(ACL Anthology)
Refusal Appropriateness Rate: % of cases where the model correctly declines to answer when it lacks grounds. Tie this to uncertainty calibration work (see below).(arXiv)
How to evaluate at scale without breaking the bank
LLM‑as‑Judge, with caveats. The MT‑Bench/Chatbot Arena team shows strong LLM judges can reach “over 80% agreement” with human preferences—roughly human‑to‑human agreement levels—if you mitigate known biases (e.g., position, verbosity). Use this to triage evaluations, then calibrate with smaller human gold sets.(arXiv)
Design guardrails that actually reduce hallucinations
Grounded generation (RAG). Retrieval‑augmented models are consistently more specific and factual on knowledge‑intensive tasks than parametric‑only baselines.(arXiv)
Uncertainty‑aware UX. Research shows entropy‑based and rank‑calibration approaches can flag likely confabulations; expose confidence/citations in UI, route “low‑confidence” answers to human review or a slower, more cautious chain.(Nature)
Pull‑quote: “RAG models… generate more specific, diverse and factual language than a state‑of‑the‑art parametric‑only seq2seq baseline.” (NeurIPS Proceedings)
2) Helpfulness: measure outcomes, not vibes
Definition (product‑level): How effectively the AI accomplishes the user’s goal in context, adjusted for effort and time saved.
Recommended offline metrics
Task Success Rate (TSR): Human or LLM‑judge rating of whether a task is completed to spec (pairwise or rubric‑based). LLM‑as‑judge can scale here, but keep a human‑labeled canary set for calibration.(arXiv)
Benchmark fit: Use domain‑relevant benchmarks (e.g., MT‑Bench for dialog quality; domain suites for legal/medical if applicable; HELM scenarios to track multi‑metric performance across accuracy, calibration, robustness, toxicity).(arXiv)
Recommended online/production metrics
Time‑to‑Task (TtT) and Time Saved: Measure flow completion time with/without AI assistance (A/B or within‑subject).
Human Acceptance Rate: % of AI drafts accepted with zero/low edits (e.g., document draft acceptance, code suggestion acceptance, support reply sent as‑is).
Downstream Impact: For coding, time‑to‑merge and post‑merge defect rate; for support, first‑contact resolution; for sales, qualified meetings booked.
Evidence to set initial targets
In a controlled experiment, developers with GitHub Copilot completed a coding task 55.8% faster than the control group.(arXiv)
In knowledge‑work settings (e.g., writing and customer support), randomized and field studies find meaningful productivity gains and improved worker experience from generative AI assistance.(Science)
These effects won’t copy‑paste to your product, but they justify instrumenting time saved and acceptance as first‑class metrics.
Quality isn’t just “did it work?”
Track appropriateness (did the assistant choose the right tool or escalate?) and harmlessness/safety (did it avoid unsafe content). Alignment work (e.g., training helpful and harmless assistants with RLHF) helps, but efficacy is scope‑ and policy‑dependent—and should be measured in your scenario.(arXiv)
3) Latency: what users feel first
Definition (product‑level): The experienced responsiveness of your AI feature. For interactive chat or copilots, prioritize:
TTFT (Time‑to‑First‑Token): Time until the first visible token/word streams back—perceived “instant response.”
TPS (Tokens‑per‑Second) / TPOT (Time‑per‑Output‑Token): Streaming speed once output begins.
TTLT (Time‑to‑Last‑Token): Total time to finish the response.
Authoritative definitions note TTFT “includes request queuing, prefill, and network latency” and grows with prompt length due to KV‑cache construction.(NVIDIA Docs) Databricks expresses overall response latency as Latency ≈ TTFT + (TPOT × #output tokens)—a useful, PM‑friendly mental model.(Databricks)
Why it matters to business outcomes
Speed is a growth lever. Even outside AI, a Google/Deloitte study found that improving mobile load times by 0.1s raised conversion progression across the funnel. Tiny wins in perceived speed change behavior.(web.dev)
How to improve it (and what to measure)
Prompt Caching: Reuse precomputed prompt segments; OpenAI reports up to 80% latency reduction and up to 75% cost reduction when caching long, repeated prefixes. Track cache hit rate and its effect on TTFT.(OpenAI Platform)
Continuous batching + PagedAttention (vLLM): Modern serving stacks increase throughput 2–4× at comparable latency (and much higher in some vendor reports), especially on long contexts. Monitor p50/p95 TTFT and TPS after changes.(arXiv)
Streaming by default: Ship tokens as soon as they’re ready; optimize TTFT even if TTLT remains constant so the interface feels alive. (AWS/Azure/NVIDIA documentation emphasizes TTFT as the key to perceived responsiveness.)(Amazon Web Services, Inc.)
Putting it together: an AI balanced scorecard you can ship
Below is a compact, four‑quadrant scorecard tailored to AI features. (The first three quadrants are the Trinity; the fourth tracks risk.)
A. Hallucination (Trust & Grounding)
Primary: Unsupported Claim Rate (UCR); FActScore (offline); TruthfulQA (offline stress test).(ACL Anthology)
Secondary: Citation Coverage/Precision; Refusal Appropriateness Rate; QAFactEval for summarization.(ACL Anthology)
Targets to try: UCR < 3% for customer‑facing answers with citations; Refusal Appropriateness > 95% in “unknown” canary prompts.
B. Helpfulness (Task Outcomes)
Primary: Task Success Rate (rubric or pairwise), Time Saved, Acceptance Rate (drafts accepted with light edits).
Secondary: Downstream quality (defect rate, first‑contact resolution), user‑rated usefulness.
Targets to try: ≥ 15–30% median time saved on key flows; ≥ 60% acceptance for low‑risk drafts (tuned per domain). (Use your own baseline; research suggests bigger gains are possible in some contexts.)(Science)
C. Latency (Experience & Flow)
Primary: p50/p95 TTFT, p50/p95 TTLT, TPS.
Secondary: Abandonment during generation, Interrupt Rate (user stops generation).
Targets to try: p50 TTFT < 300–500 ms for interactive chat; p95 TTLT aligned to task length (e.g., < 5–7 s for typical responses). (Set higher budgets for retrieval‑heavy or complex tasks; stream early.)(NVIDIA Docs)
D. Risk & Safety (Cross‑cutting)
Primary: Toxicity/Policy Violation Rate, Bias/Representation checks, PII leakage rate, and Calibration gap(see below).
Secondary: Assist escalation rates (to human/higher‑precision pipelines).
Don’t forget calibration: confidence you can trust
A factual‑looking sentence with overconfident certainty language is a trust killer. Pair hallucination metrics with calibration metrics so the system knows when to hedge or escalate.
Rank‑Calibration Error (RCE): Evaluate whether lower uncertainty correlates with higher answer quality across outputs; proposed to address limitations of AUROC/ECE for generation.(arXiv)
Brier score / ECE: Where you elicit explicit probabilities (“I’m 70% confident”), measure the gap between stated confidence and actual correctness. (ECE/Brier are standard measures for probability calibration.)(OpenReview)
Confabulation detection via uncertainty: Recent work shows entropy‑based estimators can flag a subset of hallucinations in open‑ended Q&A. Use these signals to route to safer plans (e.g., search‑augmented chains or human review).(Nature)
How to build the evaluation pipeline (a practical sequence)
Offline “truth first” suite
Create a canonical evaluation set: 200–1,000 real prompts from your product, plus edge cases.
Score with FActScore/QAFactEval for grounding, LLM‑as‑judge for helpfulness, and track latency on recorded runs. Verify a subset with humans to calibrate the judges (MT‑Bench shows this can work when biases are mitigated).(ACL Anthology)
Online canaries & causal reads
Ship a small‑traffic slice instrumented to collect UCR, Acceptance, TTFT/TTLT, and abandonment.
Add counterfactual logging where possible (e.g., “what would have happened without the AI draft?” via matched tasks) to estimate time saved.
Serve for speed, not just throughput
Turn on streaming, prompt caching, and a modern server (continuous batching + KV‑cache paging). Watch p95 TTFT; users feel that first token more than your aggregate throughput.(OpenAI Platform)
Close the loop
When UCR spikes, auto‑file examples to a RAG‑tuning queue (retriever boosts, corpus fixes).
When calibration gap widens (e.g., high confidence + low factuality), adjust decoding temperature, add uncertainty‑aware refusal, or route to a slower, verified chain.(arXiv)
Trade‑offs and anti‑patterns
“Faster model, worse answers.” High throughput serving (e.g., aggressive batching) can help cost, but watch for TTFT regressions that hurt perceived speed. Always track TTFT/TPS separately.(ACM Digital Library)
“More helpful, less honest.” RLHF can increase helpfulness but distort calibration; verify with RCE/ECE and enforce refusal on low‑confidence spans.(ACL Anthology)
“Citations as decoration.” A link that doesn’t actually support the claim increases perceived trust while reducing real trust. Measure Citation Precision, not just presence. (QA‑based checks help.)(ACL Anthology)
Example: a one‑page Trinity scorecard (what great PM dashboards show)
Hallucination/Trust
UCR: 1.7% (target < 3%); Citation Precision: 95%; FActScore (offline): 0.82
Helpfulness/Outcomes
Task Success (rubric): 4.3/5; Time Saved (median): +24%; Draft Acceptance (light‑edit): 63%
Latency/Experience
p50 TTFT: 320 ms; p95 TTFT: 780 ms; p95 TTLT: 6.4 s; TPS (streaming): 32 tok/s
Risk & Calibration
Toxicity violations: 0.02%; ECE: 0.07; Refusal Appropriateness: 97%
Finally, annotate the dashboard with what changed last release: “Enabled prompt caching → p50 TTFT −41%,” “Retriever corpus update → UCR −0.9 pp,” etc.(OpenAI Platform)
Conclusion
A Trinity‑first scorecard reframes AI development around what users actually perceive: Did it tell the truth? Did it help me? Did it feel fast? That’s the backbone of trust.
Start with an offline suite (FActScore/QAFactEval + LLM‑as‑judge), stand up production telemetry (UCR, acceptance, TTFT/TTLT), and iterate with grounded generation, prompt caching, and calibration‑aware routing. The result is a product that’s not just “intelligent,” but reliably useful—and one that earns the right to scale.