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Prompt Engineering Services | LLM Systems
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
Recent outcomes
AI assistant · Healthcare
20% faster decisions
Built the system prompts and evaluation framework for a patient-monitoring AI assistant running on Claude via AWS Bedrock. Clinicians reached decisions faster.
AI OCR · Multi-site retail
20,000+ docs/day tested
Designed the extraction prompts and output validation for a document-processing pipeline that cleared 20,000+ receipts in a single day of real-world testing.
4.9
on ClutchThe problem
Sound familiar?
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?
Short answer
RaftLabs builds production prompt systems for LLM applications for teams in the US, UK, Europe, Canada, and the UAE. We design system prompt architecture, few-shot libraries, chain-of-thought reasoning, RAG grounding, output validation, and evaluation frameworks. A focused engagement ships a validated v1 in 4-6 weeks; full multi-feature systems run 8-12 weeks, at a fixed cost.
Key takeaways
- RaftLabs builds production-grade prompt systems for LLM applications and has shipped production AI products since 2015 for teams in the US, UK, Europe, Canada, and the UAE.
- A focused engagement covering one use case ships a validated v1 in 4-6 weeks; full multi-feature prompt systems run 8-12 weeks.
- Focused engagements covering one use case, one model, system prompt design, few-shot library, and evaluation framework typically cost $8,000-$20,000.
- Prompt systems are built across GPT-4o, Claude, Gemini, Llama, Mistral, and other frontier or open-source models.
- You own all deliverables outright including system prompts, few-shot libraries, evaluation test sets, and documentation.
Trusted by
Prompt engineering, by the numbers
- every prompt scored before it ships
- Eval-first
- to a validated v1 prompt system
- 4-6 weeks
- rated by clients on Clutch
- 4.9/5
- shipping production AI products
- Since 2015
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 is the engineering around the prompts: clear structure, hard constraints, worked examples, output validation, and a systematic way to measure performance before you ship.
- ≥30%
- of generative AI projects are abandoned after proof of concept
- Gartner, July 2024
Gartner ties that failure rate to poor data quality, weak risk controls, escalating cost, and unclear business value. The model is rarely the reason. What is usually missing is a structured prompt system and an evaluation framework that proves the LLM performs on real production inputs, not cherry-picked demo cases.
Prompt engineering is what separates AI products people trust from ones they quietly stop using.
Capabilities
What we build
- 01System prompt architecture
Structured system prompts that define the model's role, hard constraints, output format, and domain context, with a modular architecture so each part can be updated independently. Constraints are worded to survive adversarial inputs, and large context is compressed with retrieval instead of static injection.
- Built with
- JSON Schema · RAG
- 02Few-shot example libraries
Curated input/output example libraries that demonstrate the correct behavior for your use case, selected to cover the edge cases that trip up zero-shot prompting. Dynamic few-shot selection retrieves the most relevant examples for each query rather than including a fixed set.
- 03Chain-of-thought prompt design
Prompts that guide the model through explicit reasoning steps before a final answer, effective for multi-step problems, numerical reasoning, and structured decisions. The intermediate reasoning is validated before it reaches the user, catching wrong answers before production.
- 04Tool use and function calling
Tool and function definitions designed so the LLM reliably selects the right tool, passes the right parameters, and handles results correctly across database queries, API calls, and external lookups. The function calling layer that makes your AI agent reliable rather than unpredictable.
- 05Output validation and guardrails
Structured output parsing, schema validation, and semantic guardrails that catch non-conforming outputs before they reach the user, with retry logic for outputs that fail validation. Queries the model can't reliably answer fall back to human support rather than a confident wrong answer.
- 06Prompt evaluation frameworks
Evaluation test sets, automated scoring pipelines, and metrics dashboards that measure prompt performance on your real distribution of user inputs. Regression tests run before any prompt reaches production, and the same harness tracks cost per call and latency so a reliability gain never quietly triples your token bill. Drift monitoring flags the day a provider silently updates a model.
How we work
From problem to optimized prompts
Prompt engineering engagements follow a structured four-phase process. Every phase produces an artefact you own.
- Week 101
Use case audit and goal definition
We map the tasks you want the LLM to handle, the failure modes you are experiencing, and the quality thresholds you need to hit. We define evaluation criteria before writing a single prompt.
- Weeks 2-302
Prompt design and baseline evaluation
We design the system prompt architecture, few-shot examples, and output format specifications. We run the prompts against your real inputs and measure against the criteria defined in week 1.
- Weeks 3-503
Iteration and evaluation
We iterate based on evaluation results, edge cases, and failure mode analysis. Chain-of-thought, structured outputs, and RAG grounding are added where they improve reliability. Each round is scored against the same baseline, so you can see the reliability curve move.
- Week 604
Handover and documentation
We deliver a complete prompt library with usage guidelines, evaluation test sets, and documentation on how to iterate prompts as your use case evolves. You own everything.
Prompt systems built for production, not demos
Structured prompts, few-shot libraries, evaluation frameworks, and output validation. Fixed cost delivery.
Process
How we approach prompt engineering
- 01Evaluation framework first
Before writing a single prompt, we define what success looks like: the accuracy targets, format requirements, and edge cases that matter. We build the evaluation framework first, then design prompts to pass it, rather than optimizing against examples that don't represent real production inputs.
- 02Domain and user population analysis
We analyze your domain vocabulary, real user inputs, and edge cases before designing any prompts. A legal document analysis system needs fundamentally different grounding, formats, and failure modes than a customer support chatbot, so the right design starts from your specific context.
- 03Iterative optimization against evaluation
We design, evaluate against the test set, identify failure modes, redesign, and re-evaluate in cycles. Each iteration targets a specific failure mode, and we continue until the prompts pass the evaluation thresholds for your use case.
- 04Model-specific optimization
Different LLMs respond differently to the same prompt, so we optimize for your specific model choice and evaluate across versions when you need portability. Cost-performance trade-off analysis is included if you're choosing between model options.
- Built with
- GPT-4o · Claude · Llama
Why us
Why teams choose RaftLabs
- 01Senior engineers build what they scope
The engineers who assess your prompt system also build it. No bait-and-switch, no offshore handoff after the contract is signed. The team you meet in week one is the team that ships.
- 02Fixed price before development starts
We scope the work, calculate the cost, and lock it in writing before any development starts. A scope change is a change request: priced, agreed, or dropped. It never absorbs into the project and appears on the final invoice.
- 03Shipping production AI since 2015
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. A track record across AI, SaaS, mobile, and automation platforms in healthcare, fintech, logistics, and hospitality.
- 04Evaluation-first: prompts measured against your real data
We build the evaluation framework before writing a single prompt. Each prompt revision is scored against your specific distribution of real user inputs, not cherry-picked examples. You see exactly what improved and what regressed before anything reaches production.
LLM outputs that are reliable, not impressive in demos
Production prompt systems with evaluation frameworks that measure real performance. Fixed cost.
What clients say
What clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Amer Abu Khajil
CanadaFounder, Peak Studios & Perceptional
I found RaftLabs to be the perfect partner for Perceptional, with their expertise in helping startup founders build MVPs, a free consultation, a prototype that matched my vision, and their unwavering support.
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Read moreFrequently asked questions
Prompt engineering is the practice of designing, structuring, and optimizing 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 behavior. 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, behavior 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. Full prompt systems covering multiple AI features, multi-model evaluation, RAG integration, and ongoing prompt optimization 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.
A focused engagement covering one use case ships a validated v1 in 4-6 weeks: 1 week for the evaluation framework and domain analysis, 2-3 weeks of iterative prompt design and testing, and 1 week for final validation and handoff documentation. From there you iterate on live data. Full prompt systems for multi-feature AI products run 8-12 weeks. We deliver a fixed scope at a fixed price, so the timeline is agreed before development starts.
We work across GPT-4o, GPT-4o mini, Claude 3.5 Sonnet, Claude 3 Haiku, Gemini 1.5 Pro, Llama 3, and Mistral. Model-specific optimization matters: the same prompt behaves differently across providers, especially on edge cases, format compliance, and refusal behavior. If you are choosing between models, we include cost-performance trade-off analysis in the evaluation framework so you can see the differences on your actual use case data.
Yes. We sign mutual NDAs before any detailed scoping conversation. Prompt systems and system prompts are proprietary intellectual property, and we treat them as such. You own everything we build: the system prompts, the few-shot libraries, the evaluation test sets, and all documentation. Nothing is reused for other clients.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Prompt Engineering Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.