Prompt Engineering Services

Structured system prompts that define the model's role, hard constraints, output format requirements, and domain context. Prompts designed to be consistent across diverse user inputs, not optimised for the examples you thought of. Modular prompt architecture that separates role definition, task instructions, format requirements, and domain grounding so each can be updated independently.

System prompt architecture decisions that are not obvious: (1) Role definition tone affects output register, "you are a helpful assistant" produces different output formality from "you are a senior analyst at a regulated financial institution." (2) Hard constraints expressed as negatives require different treatment from positive instructions, "never recommend specific investments" needs explicit wording to survive paraphrase attacks and adversarial inputs. (3) Output format instructions expressed as JSON Schema or XML with field descriptions produce more reliable structured output than natural language format descriptions, especially for optional fields and nested structures. (4) Domain grounding injected into the system prompt keeps the model from defaulting to generic internet knowledge, for a support assistant for a specific software product, every entity the model needs to reference is defined in the system prompt context. Prompt length management: large context costs more to process on every call. We audit system prompts for redundancy and compress domain grounding using retrieval (RAG) rather than static injection where the context volume is large.

Curated libraries of input/output examples that demonstrate the correct behaviour for your specific use case. Examples selected to cover the edge cases that trip up zero-shot prompting, unusual phrasings, ambiguous requests, domain-specific terminology, and format requirements. Dynamic few-shot selection that retrieves the most relevant examples for each user query rather than including a fixed set.

Prompts that guide the model through explicit reasoning steps before producing a final answer, effective for multi-step problems, numerical reasoning, logical analysis, and structured decision-making. Chain-of-thought designs that produce intermediate reasoning the system can validate before showing the user, catching wrong answers before they reach production.

Tool and function definitions for models that support tool calling, designed so the LLM reliably selects the right tool, passes the right parameters, and handles tool results correctly. Tool definitions for database queries, API calls, calculation functions, and external lookups. The function calling layer that makes your AI agent reliable rather than unpredictable.

Structured output parsing, schema validation, format enforcement, and semantic guardrails that catch outputs that don't meet requirements before they reach the user. Retry logic with refined prompts for outputs that fail validation. Fallback handling for queries that the model can't reliably answer, directing users to human support rather than producing a confident wrong answer.

Evaluation test sets, automated scoring pipelines, and metrics dashboards that measure prompt performance on your actual distribution of user inputs. Regression testing that runs before every prompt change. Performance tracking over time to detect model drift when providers update their models. The measurement layer that turns prompt engineering from guesswork into engineering.

Before writing a single prompt, we define what success looks like for your specific use case, the accuracy targets, format requirements, and edge cases that matter. We build the evaluation framework first, then design prompts to pass it. This prevents the common failure mode of optimising prompts against examples that don't represent real production inputs.

We analyse your specific use case, domain vocabulary, user inputs (if available), and edge cases before designing any prompts. Prompts for a legal document analysis system look fundamentally different from prompts for a customer support chatbot, different domain grounding, different format requirements, different failure modes. The right prompt design starts with understanding the specific context.

Prompt engineering is iterative. We design, evaluate against the test set, identify failure modes, redesign, and re-evaluate, in cycles. Each iteration improves a specific failure mode identified in evaluation. We continue until the prompts pass the evaluation thresholds for your use case.

Different LLMs respond differently to the same prompts, what works for GPT-4o may need adjustment for Claude or Llama. We optimise prompts for your specific model choice and evaluate across model versions when you need portability or are evaluating which model to use for a given task. Including cost-performance trade-off analysis if you're choosing between model options.