Prompt Engineering is for teams that need a result they can measure. The difference between an AI feature that impresses users and one that embarrasses them is almost always the prompt engineering. We are specialists in designing, testing, and optimising the system prompts, RAG architectures, and AI pipelines that determine what your product's AI actually does. From a 30-minute audit to a full AI system redesign, we make your AI work the way it was supposed to.
A typical engagement includes System Prompt Audit & Redesign, Prompt Testing & Evaluation, and RAG Pipeline Optimisation. Delivery focuses on Diagnostic Audit (Week 1) and Redesign & Testing (Weeks 2–3). You also get Measurable improvement in AI output quality, Automated test suite for every prompt, and Typical 30–60% reduction in token costs.
Senior engineers who have shipped similar systems do the work. Common technical work includes Hallucinations & Inaccuracies, Inconsistent Output Format, and Off-Brand or Unsafe Responses. Open the sections below for scope, or contact us with the outcome you need.
We review your existing system prompts, identify failure modes and edge cases, and rewrite them using proven prompt engineering patterns — chain-of-thought, few-shot examples, role definition, output constraints.
We build automated evaluation suites that test your prompts against hundreds of real-world inputs — measuring accuracy, consistency, safety, and cost per query before any prompt goes to production.
Retrieval-Augmented Generation systems often degrade because the retrieval is poor, not the generation. We audit your chunking strategy, embedding model, retrieval logic, and prompt construction to maximise accuracy.
Poorly designed prompts waste tokens. We redesign your AI pipeline to use the most cost-effective model for each task — routing simple queries to cheaper models while reserving frontier models for complex ones.
A senior-level review of your entire AI feature architecture — model selection, context management, output parsing, error handling, and monitoring — with a detailed remediation report.
A complete, version-controlled library of your production prompts with documentation, test cases, and change management guidelines — so your team can maintain and improve prompts confidently.
AI giving wrong answers confidently. We identify the root cause — missing context, poor retrieval, inadequate output constraints — and fix it systematically.
AI returning different formats on different runs, breaking downstream parsing. We implement strict output schemas with validation and retry logic.
AI going off-topic, using wrong tone, or occasionally producing inappropriate content. We implement guardrails, persona anchoring, and input/output filtering.
AI features consuming 10× more tokens than necessary. We compress prompts, implement caching, and route queries to cost-appropriate models.
We run your existing prompts through a structured evaluation framework — identifying failure modes, inconsistencies, and quick wins within the first week.
We redesign the problem areas, build an automated test suite, and run A/B comparisons between old and new prompts — with measurable quality metrics.
Prompts deployed with logging, quality monitoring, and drift detection — so you know immediately if output quality degrades after deployment.
A practical workshop for your engineering and product team on prompt engineering best practices — so they can maintain and extend the system independently.
Each engagement is scoped to your goals, but most projects include System Prompt Audit & Redesign, Prompt Testing & Evaluation, RAG Pipeline Optimisation, and AI Cost Optimisation. We tailor deliverables to your product stage, team capacity, and business priorities.
Project timelines depend on complexity, integrations, and existing systems. Most engagements start with discovery and scoped planning, followed by iterative delivery milestones so you can review progress and ship value early.
Our stack is chosen based on your context, and commonly includes Hallucinations & Inaccuracies, Inconsistent Output Format, Off-Brand or Unsafe Responses, and High Token Costs. We prioritize maintainability, performance, and long-term ownership by your internal team.
We follow a delivery process centered on Diagnostic Audit (Week 1), Redesign & Testing (Weeks 2–3), and Deployment & Monitoring and back it with Measurable improvement in AI output quality, Automated test suite for every prompt, and Typical 30–60% reduction in token costs. You receive clear documentation, production-ready handover, and ongoing support options after launch.