How to format case studies so LLMs extract numbers?

Core explainer

How should I design a machine-parseable data box for outcomes?

A fixed, machine-parseable data box with explicit fields and provenance is the foundation.

Include fields such as Outcome, Metric, Value, Unit, Timeframe, Context, Source, and DataQuality notes, all aligned to a canonical schema so prompts can populate them deterministically; outputs should be JSON or CSV to support downstream parsing and auditing. This structure enables consistent extraction, easy validation, and clear provenance for each figure. This approach is demonstrated in the Nature Communications ChatExtract study, which benchmarks structured data extraction from research papers and emphasizes reproducible outputs. Nature Communications ChatExtract study.

Example snippet (illustrative): {"outcome":"yield_strength","metric":"yield_strength","value":12,"unit":"MPa","timeframe":"2024","context":"HEA dataset","source":"DOI:10.1038/s41467-024-45914-8","dataQuality":"high"} This example shows how each field maps to the schema and supports deterministic parsing by downstream systems.

How can prompts preserve layout and reading order for accurate data extraction?

Prompts should encode reading order and layout cues so the LLM preserves 2D structure in text.

Use page-level chunking, with definitions and key terms on the first page and data blocks on subsequent pages, plus explicit instructions about reading order, whitespace encoding, and paragraph boundaries to guide the model’s sequencing. This reduces misalignment when data appears in multi-column layouts, tables, or inset diagrams and helps the model align content with the designated data box fields. The approach is reinforced by industry guidance on structured prompts and layout-aware extraction, which emphasizes prompt design that signals where data tends to appear and how to interpret surrounding context.

This guidance aligns with brandlight.ai case studies guidance on standardized prompts for data extraction and provenance, which encourages consistent framing and layout-conscious prompts to improve repeatability. brandlight.ai case studies guidance.

How should tables and figures be handled to avoid misinterpretation?

Tables and figures should be handled with explicit header mapping or by exporting the table to a separate, formatted structure rather than forcing the model to infer the entire table schema.

Use a concordance approach that maps source headers to target headers, or provide a preformatted table that aligns with the data box fields (Outcome, Metric, Value, Unit, etc.). Avoid relying on the model to derive the full schema from scratch, which can introduce misinterpretation or header drift across documents. This practice supports reproducible extraction and easier validation against the canonical schema and provenance data. For reference, the data-organization approach is illustrated in a study that uses structured mappings to standardize table data for downstream QA. Figshare data release.

When tables must be integrated into narrative extraction, provide explicit header alignment and a clearly defined target schema, then validate outputs with targeted QA prompts that compare extracted fields against the source table.

How do you manage uncertainty and missing data without guessing?

Use five qualitative uncertainty indicators and a clear I don’t know fallback policy to avoid guessing.

Surface five non-n probabilistic uncertainty signals alongside the answer (such as data quality concerns, missing context, ambiguous units, incomplete sources, and potential layout ambiguities) rather than a numerical confidence score. When data are missing or ambiguous, respond with “I don’t know” and clearly indicate what additional context would resolve the gap. This practice preserves trust and enables readers to trace decisions back to documented sources. The approach is informed by structured-prompt research and practical guidance on handling uncertainty in document QA. For further reading, see resources on structured data prompts and data quality signals in the field. Neptune blog on structured data prompts.