Prompt Engineering for Insurance
Systematic prompt design and evaluation frameworks that make AI model outputs reliable, consistent, and cost-efficient in production.
Prompt Engineering
Prompt engineering is the practice of designing and systematically optimising the inputs given to language models to produce consistent, accurate, and cost-efficient outputs. It covers template design, few-shot example curation, chain-of-thought structuring, and automated evaluation.
Insurance
Insurance technology for underwriting automation, claims processing, and policy management — reducing manual overhead across the full policy lifecycle.
How we deliver Prompt Engineering
Prompts are code — treat them that way
A poorly engineered prompt is the most common reason AI pilots fail to reach production quality. Inconsistent outputs, hallucinated facts, wrong formats, and unpredictable costs all trace back to how the model is being asked to behave. We build prompts that are versioned, tested, and evaluated — the same way you would treat application code.
Our process starts with output specification: defining exactly what a correct response looks like, including format, tone, factual constraints, and failure modes. From there we design prompt templates, curate few-shot examples, and build automated evaluation pipelines that score outputs against defined criteria — so you know when a prompt change improves or regresses quality.
We also work on cost optimisation: selecting the smallest model that meets quality requirements, structuring prompts to minimise token usage, and caching responses where output is deterministic. On high-volume applications, these decisions typically reduce inference costs by 40–70%.
Key capabilities for Insurance
Technologies we use
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