AI, Data & Intelligence
Prompt Engineering
Systematic design, testing and versioning of the instructions that drive language model behaviour, so outputs are accurate, consistently formatted and stable when models or inputs change.
Capability overview
What prompt engineering involves
Prompts are production code. A small wording change can shift accuracy, tone or output format across thousands of requests, and a model upgrade can quietly change how an old prompt behaves. Prompt engineering treats instructions as versioned, reviewed and tested assets rather than text edited in a playground.
We design prompts with clear role and task definitions, explicit constraints, carefully chosen examples and structured output formats enforced through JSON schemas or tool calling. Every prompt has a test set, so changes are accepted only when they improve results without breaking cases that previously worked.
What is included
What prompt engineering covers
Prompt design
Instructions, context layout, examples and output formats written for the specific model family and task.
Structured outputs
Schema-constrained responses and validation so downstream code receives predictable fields instead of free text.
Prompt audits
Existing prompts reviewed for ambiguity, conflicting instructions, token waste and exposure to prompt injection.
Version control and templates
Prompts stored in repositories or prompt management tools with variables, change history and environment promotion.
Regression testing
Automated test suites that run prompts against expected behaviours whenever prompts or models change.
How we work
How we deliver prompt engineering
Task specification
Desired behaviour, output format and unacceptable outputs written down with examples from real inputs.
Test case collection
Typical, edge and adversarial inputs assembled into an evaluation set.
Iterative drafting
Prompt variants compared on the evaluation set using automated checks and human review.
Cross-model testing
The prompt checked on alternative models to understand portability and fallback options.
Release and monitoring
Approved version deployed with logging so production behaviour can be compared with test results.
Related capabilities
Related capabilities in Generative AI & LLM Engineering
LLM Integration
Adding large language model capabilities to existing applications through provider APIs, handling streaming, structured output, rate limits, retries, data redaction and switching between providers.
Model Fine-Tuning
Adapting language models to your tasks, terminology and output formats with supervised fine-tuning and parameter-efficient methods such as LoRA, when prompting and retrieval are not enough.
Vector Database Solutions
Selection, design and operation of vector databases for AI search and retrieval, from pgvector inside PostgreSQL to dedicated engines such as Qdrant, Weaviate, Milvus or Pinecone, tuned for recall, speed and cost.
Semantic Search
Search that understands meaning rather than exact keywords, for websites, product catalogues, intranets and support portals, combining vector and keyword retrieval with relevance tuning and analytics.
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Questions & answers
Questions about Prompt Engineering
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionYes, although the focus has shifted. Newer models follow instructions better, but clear task definitions, structured outputs, context design and regression testing still determine reliability in production.
For many tasks it can, and it should be tried first because it is faster and cheaper. Fine-tuning becomes worthwhile when prompts grow very long, formats must be extremely consistent or costs need reducing at scale.
Yes. Workshops for developers cover prompt patterns, evaluation and versioning, while sessions for business users focus on writing effective requests to AI assistants.
Prompt audits and optimisation of a defined set of prompts are fixed price. Ongoing prompt maintenance is typically included in LLMOps support.
Next step
Discuss prompt engineering with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.