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    3. Prompt Engineering Impact

    Prompt Engineering Impact

    How prompting technique changes accuracy, completeness and relevance of legal answers.

    IllustrativeUpdated 2026-07-08

    Prompt Engineering Impact on Legal Output

    How different prompting techniques affect AI accuracy, completeness, and relevance in legal tasks. Multi-step decomposition and structured output formats yield the highest quality results.

    Key Takeaway

    Multi-step decomposition prompts achieve 91% accuracy - a 47% improvement over basic zero-shot queries. Jurisdiction-scoped prompts achieve the highest relevance at 92%, critical for cross-border legal work.

    Illustrative estimate - a directional figure for scenario framing, not a measured benchmark. Do not read these as measured per-vendor results.

    Across eight prompting techniques in this illustrative scenario, accuracy ranges from 62 on zero-shot queries to a high of 91 for multi-step decomposition, with jurisdiction-scoped prompts scoring highest on relevance (92) and structured-output prompting leading on completeness (90). Chain-of-thought and role-based framing land in the middle of the pack. The spread suggests query structure, not just model choice, is a meaningful lever for output quality - pointing legal teams toward prompt design and jurisdiction-scoping as a lower-cost improvement path than model switching. These figures are illustrative, not measured benchmarks.
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