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Tell us the AI use case, the model you're using, and what's not working in production. We'll scope the prompt system and evaluation framework.
LLM producing good results in testing but inconsistent or wrong answers in production?
No way to measure whether your prompts are actually working across the range of real user inputs?
Prompt Engineering Services
Getting an LLM to produce a correct answer in a demo is straightforward. Getting it to produce consistently correct, safe, and appropriately formatted answers across thousands of real user inputs -- with edge cases, adversarial prompts, and domain-specific requirements -- is prompt engineering.
We build production prompt systems: structured prompt architectures, few-shot example libraries, chain-of-thought designs, output validation layers, and evaluation frameworks that measure whether the prompts actually work before you deploy them.
Production prompt systems built around your specific use case, domain, and user population
Evaluation frameworks that measure prompt performance on real inputs, not cherry-picked examples
System prompt architecture, few-shot libraries, chain-of-thought designs, and tool use specifications
Works across GPT-4o, Claude, Gemini, Llama, Mistral, and other frontier or open-source models
RaftLabs provides prompt engineering services -- designing and optimising production prompt systems for LLM-powered applications. Prompt engineering services cover system prompt architecture, few-shot example libraries, chain-of-thought reasoning designs, tool use specifications, output validation layers, and evaluation frameworks that measure prompt performance on real inputs. We work across GPT-4o, Claude (Anthropic), Gemini, Llama, and Mistral. Most prompt engineering projects deliver in 4--10 weeks at a fixed cost as part of a broader AI development engagement or as a standalone optimisation project.
Good prompts are an engineering discipline, not a creative exercise
The difference between an LLM that works in demos and one that works in production is not the model -- it's the engineering around the prompts. Structure, constraints, examples, output validation, and a systematic way to measure performance before deployment.
Prompt engineering is what separates AI products that users trust from ones they stop using.
What we build
System prompt architecture
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.
Few-shot example libraries
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.
Chain-of-thought prompt design
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 use and function calling
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.
Output validation and guardrails
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.
Prompt evaluation frameworks
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.
Prompt systems built for production, not demos
Structured prompts, few-shot libraries, evaluation frameworks, and output validation. Fixed cost delivery.
How we approach prompt engineering
Evaluation framework first
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.
Domain and user population analysis
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.
Iterative optimisation against evaluation
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.
Model-specific optimisation
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.
LLM outputs that are reliable, not impressive in demos
Production prompt systems with evaluation frameworks that measure real performance. Fixed cost.
Related services
Custom AI Development -- full AI system development
Generative AI Development -- LLM-powered product development
RAG Pipeline Development -- retrieval-augmented generation systems
AI Agent Development -- multi-step AI agent development
LLM Integration -- integrating LLMs into existing products
Frequently asked questions
Prompt engineering is the practice of designing, structuring, and optimising the instructions given to large language models to produce reliable, accurate, and appropriately formatted outputs. In production, it matters because: (1) LLMs are sensitive to phrasing -- small changes in how you ask a question significantly change what the model returns. (2) Without structured prompts, edge cases produce unpredictable outputs that fail users and create support load. (3) Unstructured prompts make it impossible to measure performance -- you can't tell whether the model is improving or degrading. (4) Security -- poorly designed prompts are vulnerable to prompt injection attacks that manipulate the model's behaviour. Professional prompt engineering treats prompts as code: structured, versioned, tested against an evaluation set, and deployed with monitoring.
A system prompt is the persistent instruction set that defines the model's role, behaviour constraints, output format requirements, and domain context -- it's set by the application, not the user. A user prompt is the message the user sends in a conversation. Good system prompt design defines: what the model is (role), what it must always do (hard constraints), what it must never do (guardrails), how it should format its responses (structure), and what context it can reference (grounding data). Well-designed system prompts are the foundation of a reliable AI product. Poorly designed system prompts produce inconsistent outputs that depend more on how the user phrases their request than on the model's actual knowledge.
An evaluation framework is a set of test cases, metrics, and measurement processes that tell you whether your prompts are producing the right outputs across the real distribution of user inputs -- not just the examples that look good in demos. Without an evaluation framework, you're making prompt changes blind. You don't know if a change improved things or made something else worse. An evaluation framework defines: the test cases (a sample of real or realistic user inputs), the metrics (accuracy, format compliance, refusal rate, latency, cost per call), the passing threshold for each metric, and the process for running evaluation before any prompt change goes to production. We build evaluation frameworks as part of every prompt engineering engagement because they're the only way to know if the work is actually producing a reliable system.
A focused prompt engineering engagement -- one use case, one model, system prompt design, few-shot library, and evaluation framework -- typically runs $8,000--$20,000. Comprehensive prompt systems covering multiple AI features, multi-model evaluation, RAG integration, and ongoing prompt optimisation run higher. Prompt engineering is often scoped as part of a broader AI product development engagement rather than standalone, in which case it's included in the project cost. We scope every project before pricing it.