Factual Grounding

Every generated claim must be directly traceable to a specific passage in a source document. This requires citation accuracy—inline references that point to the exact chunk, not just the parent document. A grounded system implements provenance tracking to log the full lineage of each fact from ingestion to output, enabling auditability. Without this, the system cannot distinguish between faithful retrieval and hallucination.

The model's decoding process is strictly conditioned on the retrieved context window. Techniques include:

• Instruction-tuned prompts that forbid the model from using parametric knowledge
• Constrained decoding that penalizes tokens not supported by the context
• Cross-encoder re-ranking to ensure only the most relevant passages influence generation This transforms the model from an open-ended generator into a closed-book summarizer of provided evidence.

Retrieved documents may contain conflicting information. A grounded system must identify contradictions between sources and either:

• Flag the conflict for human review
• Apply a confidence calibration signal based on source authority and recency
• Synthesize a response that acknowledges the disagreement This prevents the model from arbitrarily selecting one source or blending incompatible facts into a hallucinated compromise.

Complex sentences are decomposed into atomic, self-contained propositions before verification. Each atomic fact is independently checked against the retrieved context. This granular approach enables:

• Precision citation at the fact level rather than the paragraph level
• Partial grounding where supported claims are retained and unsupported ones are discarded
• Integration with propositional chunking strategies for more accurate retrieval

Not all sources are equal. Grounding systems apply metadata filtering to prioritize documents based on:

• Content freshness for time-sensitive queries
• Source authority derived from domain expertise and citation networks
• Information density to favor content-rich passages over thin or redundant text This weighting ensures the model grounds its output in the most credible and current evidence available.

A properly grounded system knows when to refuse to answer. If the retrieved context lacks sufficient evidence to support any claim, the model must explicitly signal uncertainty rather than fabricate. This is implemented through:

• Confidence thresholds that trigger abstention
• Explicit uncertainty markers in the output
• Fallback retrieval with broader search parameters before final abstention This is the ultimate safeguard against hallucination.