Our AI / LLM Integration team treats prompt engineering as a discipline — with versioning, testing, and evaluation built into every client engagement.
Start With the Job to Be Done
Every prompt should encode a clear task, audience, constraints, and output format. Vague instructions produce vague results. We define success criteria before writing a single token — aligning prompts to measurable business outcomes.
Patterns That Work in Production
- Role + context + task + format — Structured system prompts for consistency
- Few-shot examples — Curated demonstrations for classification and extraction
- Chain-of-thought — Step-by-step reasoning for complex analysis
- Output schemas — JSON or structured formats for downstream automation
Test Like Software, Not Magic
We maintain evaluation suites of real user queries with expected outputs, running regression tests on every prompt change. This catches drift before it reaches users and provides audit evidence for regulated industries.
When Prompts Are Not Enough
For domain-heavy or high-volume use cases, we layer RAG for live context or fine-tune models for specialised language. Prompt engineering remains the control layer — governing tone, safety, and output structure regardless of the underlying model.