Guardrails and AI Safety
Grounding Validation — Stack
Grounding validation › Stack
Technology landscape as of 2026-10-10. Knowledge cutoff May 2026.
Selection criteria
You are buying a second judgement on every answer, so the trade is between how reliable that judgement is and how much of the response budget it consumes.
- Cost and latency per answer. Measured against the generation it follows, since a validator as expensive as the generator halves the product's throughput.
- Reliable premise length. The passage length beyond which the component's judgements decay, which is a hard property of the model and is rarely published.
- Verdict granularity. Whether it returns a decision per claim with the supporting span identified, or one score for the whole answer.
- Outcome vocabulary. Whether "not addressed" and "contradicted" are distinguishable outcomes, since conflating them hides which half of the pipeline is at fault.
- Independence from the generator. Whether a different model family is available, and whether the deployment allows running two families at once.
- Calibration surface. Whether a threshold can be set from your own labelled answers rather than from the component's default.
- Determinism. Whether the same answer and the same evidence reproduce the same verdict, which decides whether the gate can be regression-tested at all.
Options
| Option | Best for | Watch out for |
|---|---|---|
| Natural language inference cross-encoders (DeBERTa and RoBERTa models fine-tuned on MNLI and related corpora) | Sentence-level support decisions at very low cost and with reproducible output | Trained on short premise-hypothesis pairs; reliability drops sharply on long passages and on domain-specific prose |
| Purpose-built factual-consistency scorers (AlignScore, Vectara's hallucination evaluation model and comparable open models) | A calibrated score designed for summary-against-source checking, without prompt engineering | A single score per pair, so localising which claim failed requires splitting the answer yourself |
| A general model as judge with a constrained verdict | Multi-sentence synthesis, numeric reasoning, and anything the encoders were not trained to handle | Cost per answer, non-determinism between runs, and a strong pull towards judging answer quality instead of support |
| Retrieval evaluation frameworks (Ragas, TruLens, DeepEval) | Getting a faithfulness metric and a harness quickly, mainly for offline measurement | Built as evaluation tooling; using their metrics as a serving-path gate means inheriting their defaults as policy |
| Provider grounding and attribution features | Products already on one platform that want support checking without a second model to operate | Opaque thresholds, a taxonomy of outcomes you do not control, and behaviour that changes outside your release |
| Exact verification for extractive and numeric answers (string matching against the source, recomputation of totals) | Answers whose correctness is mechanical rather than semantic | Covers only the extractive portion; silently passes everything expressed as prose |
Models
The validator's independence from the generator is the property that most affects whether this check is worth running. A judge drawn from the same family has seen similar data, shares similar failure modes, and agrees disproportionately with text that family produced, so the cases where both are wrong are precisely the cases nothing else will catch. Preferring a different family is cheap insurance; preferring a smaller specialised model over a large general one is usually also correct, since the task is narrow and encoder-based inference models run at a fraction of the cost and return the same verdict every time, which is what makes a regression suite possible. Their limitation is premise length: these models were trained on short pairs, and support judgements degrade on long passages without any error being raised, so either the passages are split before judgement or a model with a demonstrated long-premise capability is used. Whichever is chosen, the threshold has to come from your own labelled answers — a few hundred responses adjudicated by hand, deliberately including the synthesis cases and the correct refusals, because those are the two populations where off-the-shelf defaults are most wrong. Report the validator's agreement with those human labels alongside any grounding metric it later produces, and re-measure it whenever the validator, the generator, or the chunking changes.