AI/ML
Systematic prompt design and evaluation frameworks that make AI model outputs reliable, consistent, and cost-efficient in production.
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What it is
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.
What you get
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
Each engagement is scoped to your requirements — these are the core capabilities we bring to the table.
Automated output evaluation pipelines
Prompt regression testing and CI integration
Token optimisation and inference cost reduction
System prompt hardening against injection attacks
Model-agnostic prompt libraries
Our process
A structured, engineering-led approach that moves from understanding your goals to a production system — with no handoff surprises.
Typical engagement
8–16 WEEKS
We map your goals, constraints, and existing infrastructure. Scope is defined and success criteria agreed before any development begins.
We design the technical approach, select the right tools, and produce a milestone-driven delivery plan with no ambiguity.
Iterative development with regular demos. Code reviews, test coverage, and documentation happen in parallel — not at the end.
Production release with monitoring setup and handover documentation. We stay close during the first weeks post-launch.
Built with
Ad-hoc prompts produce variable results. Engineered prompts are designed to produce consistent outputs across thousands of calls — with explicit formatting, constraints, fallback instructions, and evaluation metrics. The difference matters when your application depends on the model doing the right thing reliably, not occasionally.
Yes — this is often where we start. We audit existing prompts, identify where outputs are inconsistent or costly, build evaluation datasets from your production logs, and systematically improve quality while reducing token usage.
Not automatically. A prompt optimised for GPT-4o may behave differently on a future model version. We build evaluation suites that can be re-run after model updates so you can detect regressions before they reach users.
Work with us
Share what you're building — we'll respond within one business day with questions or a proposal outline.
