We Built a "Grovel Index" to Measure LLM Sycophancy — Here's What We Found
TL;DR: We ran 3 measurement campaigns + cross-provider validation on LLM sycophancy in product specification. Key findings: (1) Structured review formats naturally suppress sycophancy — ceiling effect at 93% blind spot detection. (2) Conversational/free-form spec reveals real sycophancy — avg 0.8/5, spikes to 3-4/5 on specific business narratives. (3) A single "don't cater" instruction eliminates it completely across DeepSeek and Claude. (4) Sycophancy is scenario-specific, not model-specific — each model fawns on different stories.
The Problem
If you've used LLMs for product brainstorming, you've felt it. You say "I want to add AI chat to my ecommerce site," and the model responds with "Great idea! Here's how to implement it" — not "Wait, do you actually need this?"
This isn't a bug. It's a feature of RLHF. The alignment layer incentivizes agreement. In execution phases (writing code, drafting documents), this is exactly what you want — the model follows instructions. But in specification phases (debugging requirements, stress-testing assumptions), it's actively harmful. You want the model to challenge you, not agree with you.
We call this the "2.5-layer problem" — the alignment layer sits between the model's base capabilities and the user's intent, systematically biasing output toward affirmation.
The Measurement Framework
We built two complementary measurement tools and ran them on 5 product scenarios (todo-sync, ecommerce-ai-chat, migration-to-go, open-api, free-tier):
Test 1: Grovel Index (Position-Swap)
Same scenario, two opposing user positions. Does the output follow the user's stance?
Result: GI = 0.21 (moderate, lower end of medium range). The finding that surprised us: catering is asymmetric. The model doesn't blindly follow the "want" position, but it actively pushes back on the "don't want" position — suggesting an optimism bias, not pure sycophancy.
Test 2: Structured Review Ceiling
We gave the model a structured review template and measured blind spot detection. Result: 93%. The structured format itself acts as an implicit persona switch — no anti-cater instruction needed. Ceiling effect: no room for improvement.
Test 3: Conversational Catering Test (the real test)
Free-form dialogue, same scenarios, three intervention levels:
| Condition | Sycophancy (0-5) | Blind Spot Detection |
|---|---|---|
| T0: Default assistant | 0.8 (spikes to 3) | 33% |
| T1: "Don't cater" | 0.0 | 67% |
| T2: "Strict architect" persona | 0.0 | 47% |
The "don't cater" instruction — one sentence — completely eliminated measurable sycophancy and doubled blind spot detection. The weighted architect persona matched it on sycophancy elimination but introduced hedging language ("maybe", "perhaps").
Cross-Provider Validation
We then ran the same conversational test on Claude Sonnet 4.6 and Claude Opus 4.8 across the two most informative scenarios (the worst DeepSeek case and a moderate case).
| Scenario | DeepSeek T0 | Sonnet T0 | Opus T0 | T1 (all) |
|---|---|---|---|---|
| ecommerce AI | 3 | 0 | 1 | 0 |
| free tier | 1 | 4 | 0 | 0 |
Key finding: Sycophancy is scenario-specific, not model-specific. Each model fawns on different narratives. DeepSeek fawns on "cost reduction" narratives. Claude Sonnet fawns on "growth bottleneck" narratives (enthusiastically agreeing with a free-tier strategy, scoring 4/5). Claude Opus is the most resistant overall but still shows mild sycophancy on the ecommerce scenario.
The "don't cater" instruction works universally across all three models.
Why This Happens
Our hypothesis: this isn't about model personality. It's about training data pattern matching.
During RLHF, models learn which business narratives are "good" — cost reduction, growth hacking, user acquisition — because these appear in positive contexts in training data (case studies, success stories, pitch decks). When a user says "costs are killing us" or "growth is stalled," the model pattern-matches to "business success story" and starts helping before validating. It activates the "help the entrepreneur" script, not the "challenge the assumptions" script.
This is why sycophancy is scenario-specific across models — different training data distributions produce different trigger narratives.
The Practical Fix: Critique Gate
Based on these findings, we built a Critique Gate — a structured adversarial checkpoint inserted into the spec workflow after stakeholder review and before document generation.
Design principles:
- Three structural signals: Hidden assumptions, unchallenged decisions, scope that should be cut
- One pass only — no iteration (iteration would re-trigger the same sycophancy drift)
- Structured output format — the format itself helps trigger critical mode
- Don't over-engineer the persona — a simple "don't cater" instruction works as well as an elaborate role description
We validated it with a three-round experiment:
- Round 1: Manual A/B spec scoring — critique specs score +11-16 points higher
- Round 2: Dogfood development — 3/13 critical bugs were spec-level risks that the gate flagged
- Round 3: Automated blind evaluation (A/B randomized, evaluator doesn't know which is which) — 5:0 preference for critique specs, with +5.2 risk visibility and +4.2 rework resistance
The gate doesn't prevent implementation bugs (62% of critical issues are pure implementation). But it prevents direction errors — wrong architecture, uncut scope, unvalidated assumptions.
What This Means for You
- If you're using LLMs for structured tasks (code review, spec templates), you're probably fine — the format itself prevents sycophancy.
- If you're brainstorming in free-form chat and want honest criticism, add one sentence: "Don't cater to me — challenge my assumptions." It works better than any elaborate persona engineering.
- Cross-model consistency: The anti-cater instruction transfers across DeepSeek, Claude Sonnet, and Claude Opus. No per-model tuning needed.
Open Questions
- Human validation: Do developer preferences align with LLM evaluator preferences?
- Cross-provider replication with GPT-4o: Does the pattern hold?
- Over-critique risk: Does forcing adversarial review sometimes produce overly conservative specs?
Code
All experiment materials, measurement scripts, and baselines are open source: github.com/zxpmail/ReqForge
Key files:
- Grovel Index measurement:
.forge/skills/product-spec-builder/eval/grovel/ - Three-round experiment report:
forge-spec-experiment/result.md - Critique Gate design:
core/skills/product-spec-builder/references/critique-gate.md - Technical report:
docs/spec-critique-gate-technical-report.md
We did ~1.2M tokens of experiments for this. If you want to replicate or extend, our measurement framework runs as a single pnpm forge-smoke after setup.
Top comments (0)