Missing Dots: Knowing Before the Model Is Called Whether the Context Is Enough
A check that runs before the model call, so unsupported answers stop earlier.
Preflight
Context scored before the model call
3 actions
Re-query, expand or abstain
The problem
Enterprise retrieval-augmented systems produce unsupported answers when retrieval misses. Teams need to know, before the language model is called, whether the retrieved context is sufficient.
Before and after
Unsupported answers
- Unsupported answers when retrieval misses
- No check, before the model is called, on whether the context is sufficient
Checked before the call
- Context scored before the model call
- A recommended remedy: re-query, expand or abstain
- Fewer unsupported answers, and fewer calls that cannot succeed
What we built
Context scoring
A preflight API scores whether the retrieved context is sufficient, before the language model is called.
Classifiers
Transformer classifiers exported to ONNX, with gradient-boosted features.
Recommended remedy
Recommends one of three actions: re-query, expand or abstain.
Retraining
Retrains through an automated research loop.
How the preflight check works
Retrieve
A retrieval-augmented system fetches context for a question.
Score
Before the language model is called, the preflight API scores whether that context is sufficient, using transformer classifiers exported to ONNX and gradient-boosted features.
Recommend
If the context is not sufficient, it recommends a remedy: re-query, expand or abstain.
Retrain
The scoring models retrain through an automated research loop.
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