Hack'celeration Agency · Prompting 2026The prompting agencythat designs them, tests them, versions them, engineers context, cuts token costreliable, not vibes.
A prompting agency that treats prompt engineering as production work, not playground tinkering. We design system prompts, add few-shot and chain-of-thought only where they earn their tokens, constrain output to structured JSON, and engineer the context with RAG so the model sees what it needs. Then we back it with an eval harness on your real cases, version the prompts like code, and pick the model (Claude, GPT, Gemini) that actually fits the task, so your AI features hold up under real traffic.
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GeminiA prompting agency engineers reliability, not clever one-liners.
Anyone can write a prompt that works once. Making it work on every input, measuring it, and keeping the cost in check is a different job. Here are the four things we own.
- Prompt design
System prompts that do one job, predictably
A clever one-liner in a playground isn't a production prompt. We engineer the full instruction layer: a tight system prompt, the right few-shot examples, chain-of-thought where it earns its tokens, and structured JSON output your code can parse. We pick the model that fits the task (Claude, GPT, Gemini) instead of defaulting to whatever's open, and we set temperature and stop conditions so the same input gives you the same shape of answer every time.
See a typical build - Evals & testing
Prompts you can trust because they're measured
The difference between a demo and a product is an eval harness. We build a test set from your real cases, define what good looks like, and score every prompt change against it, so you ship on evidence, not on whether one example looked nice in a meeting. When a model updates or you tweak an instruction, the harness tells you if quality moved before your users find out.
See the method - Context engineering & RAG
The right context, not the whole haystack
Most bad answers aren't a prompt problem, they're a context problem. We engineer what the model actually sees: retrieval that pulls the relevant passages (RAG), tool use that fetches live data, and a context window packed with what matters and nothing that just burns tokens. We add guardrails so the model stays on task and structured output so the result drops straight into your pipeline.
See the integrations - Prompt ops & handover
A prompt library your team can own
Prompts rot when they live in screenshots and Slack threads. We version them like code, document why each one is shaped the way it is, and track token cost per call so the bill stays predictable. We're an automation and AI agency first, so the prompts plug into how your product already works, and your team leaves able to change a prompt without breaking the eval that protects it.
See AI enablement
We engineer prompts like software, not like spells.
Most prompt work dies the same way: one example looks great in a meeting, it ships, and the edge cases surface in production with no way to tell what broke. So we treat prompting as engineering: scoped system prompts, structured output, the right context via RAG, and an eval harness that scores every change against your real cases before it goes live.
- Audit · map your AI features, the cases that break, and where prompts vs context vs model is the real problem
- Build · system prompts, few-shot, structured output, RAG and guardrails, scoped to each task
- Measure · an eval harness on your real cases so every change is scored, not guessed
- Hand over · a versioned prompt library your team can edit without breaking the evals
We ship LLM features every day.
We don't sell prompt magic. We build AI features that run on real traffic, including the ones behind this site, so we engineer prompts the way they survive production: scoped system prompts, structured output, context wired with RAG, and evals that catch a regression before your users do. That's exactly what's missing when prompting stops at a clever line in a playground.
- We ship LLM features in production, so we engineer prompts the way they survive real traffic, not the way a single playground example looks.
- Evals before opinions: we score prompt changes against your real cases, so quality is a number you can see, not a feeling in a demo.
- Honest about the limit: a better prompt can't fix bad data, a broken process or the wrong model, and some tasks need code or fine-tuning instead. We'll say so.
- You leave autonomous: the prompts, the eval harness and the docs live in your repo, so your team owns them without us.
Prompts at the core, the engineering around them.
We build the parts that turn prompting into reliable output, then connect them to how your product already runs. Here's what a real prompt engineering engagement covers.
- Setup
System & task prompts
We write the system prompt that fixes the model's role, tone and rules, plus the task prompts around it, so behaviour is consistent instead of drifting with every phrasing change.
- Setup
Few-shot & chain-of-thought
We add few-shot examples where they lift accuracy and chain-of-thought where the task needs reasoning, and we cut both where they only burn tokens without improving the answer.
- Setup
Structured / JSON output
We constrain the model to structured, schema-valid JSON your code can parse without regexes or retries, so an LLM step behaves like a reliable function in your pipeline.
- Setup
RAG & context engineering
We design retrieval and context assembly so the model sees the relevant passages and live data it needs, which fixes far more wrong answers than rewording the prompt ever does.
- Setup
Eval harness
We build a test set from your real cases and a scoring method, so every prompt change and model upgrade is measured against quality you defined, not judged by vibe.
- Setup
Prompt library & versioning
We version prompts like code, document the intent behind each, and track token cost per call, so your team can change a prompt safely and keep the bill predictable.
We diagnose your AI feature, you leave with a plan.
Before quoting anything, we take 60 minutes to look at your AI features, the cases where they break, and whether it's the prompt, the context or the model that's really at fault. You leave with an honest read on what to fix first and what an eval harness would catch. Zero pitch, just an engineer's take on your prompts.
- An honest read on whether it's the prompt, context or model
- The prompts and evals worth building first
- Where RAG or structured output fixes more than rewording
- A frank take on what a better prompt won't fix
How we run a prompt engineering engagement.
Five steps, in order. We don't rewrite prompts before we know the real cause, we don't ship a change without scoring it against the evals, and your team owns the library at the end. Each step has a deliverable and you sign off before we move on.
- Step 1 · Prompt audit
Find whether it's the prompt, the context or the model
We look at your AI features and the cases where they go wrong: hallucinations, inconsistent formats, answers that ignore your data, costs that creep. Half the value is the diagnosis. Often the fix isn't a smarter prompt, it's retrieval, a process change, or a different model, and we'll tell you that before you pay us to rewrite instructions that were never the problem.
- Step 2 · Engineer the prompts
Build the instruction layer that holds up
We design the system prompt, the few-shot examples, and chain-of-thought where it earns its place, then constrain the output to structured JSON your code can parse. We pick the model that fits the task and set temperature, stop conditions and guardrails so the same input returns the same shape of answer. Each prompt is scoped to one job, not a paragraph trying to do five.
- Step 3 · Wire the context
Give the model what it needs to be right
Most wrong answers are a context problem, not a wording problem. We engineer retrieval (RAG) so the model sees the relevant passages, add tool use to fetch live data, and assemble the context window to carry what matters and drop what just costs tokens. Guardrails keep it on task, and structured output means the result flows straight into your pipeline.
- Step 4 · Build the evals
Measure quality so you ship on evidence
We build a test set from your real cases and define what a good answer looks like, then score every prompt and model change against it. When Claude, GPT or Gemini ships an update, the harness tells you if quality moved before your users do. You stop shipping on a single nice example and start shipping on numbers you can defend.
- Step 5 · Hand over the library
Version it, document it, then get out of the way
We version the prompts like code, document why each is shaped the way it is, and track token cost per call so the bill stays predictable. Your team can change a prompt and the eval harness catches a regression before it ships. If you want to go deeper, our AI training covers prompting, evals and context engineering end to end so you build the next feature without us.
We're judged on the features that hold up.
No partner badge to display, so we lead with what matters: feedback from the teams whose AI features we engineered the prompts for, and whether those features stayed reliable after we left. Our Trustpilot reviews come from those teams, not from a marketing deck.
- The prompts and evals live in your repo, owned by your team
- Every prompt change scored before it touches a user
- Context engineered with RAG, output constrained to JSON
- Trustpilot reviews come from the teams we built prompts for
The questions we get asked on repeat.
What does a prompting agency actually do?
A prompting agency engineers the instruction layer behind your AI features so they're reliable in production, not just impressive in a demo. We design system prompts, few-shot examples and structured JSON output, wire the context with RAG and tool use, pick the model that fits the task, and build an eval harness that scores every change against your real cases. We also version the prompts and track token cost. The point is AI features your users can trust, not prompts that work once and break on the next input.How is prompt engineering different from just writing a good prompt?
Writing a good prompt gets you a nice answer once. Prompt engineering gets you the same quality on the thousandth call, across the inputs you didn't think of. That means a tight system prompt, few-shot and chain-of-thought used only where they help, structured output your code can parse, the right context fed in via RAG, guardrails, and an eval harness that proves a change improved things instead of quietly breaking an edge case. It's the difference between a clever sentence and a component you can ship.When is a prompting agency NOT the right fit?
When the problem isn't the prompt. A better prompt can't fix bad or missing data, a broken process upstream, or the wrong model for the job, and we'll tell you that in the audit instead of selling you a rewrite. Some tasks need code, a retrieval pipeline, or fine-tuning rather than a smarter instruction. If your feature fails because the model never sees the right context, no amount of prompt polishing will save it. We'd rather scope the real fix than bill you for the wrong one.What is an eval harness and why does it matter for prompting?
An eval harness is a test set of your real cases plus a way to score how well a prompt handles them. It matters because without it you're shipping on vibes: one example looked good, so it goes live, and you find the regressions in production. With evals, every prompt change and every model update (Claude, GPT, Gemini) is scored against quality you defined, so you ship on evidence. It's the single biggest reason production LLM features stay reliable while playground prompts fall apart.Can you help cut our token and model costs?
Yes, and it's often the fastest win. We track token cost per call, trim context that burns tokens without improving the answer, cut chain-of-thought where it isn't earning its keep, and pick a cheaper model for the steps that don't need the flagship. Structured output reduces retries, and a tighter prompt means fewer wasted tokens per request. We optimise cost against the eval harness, so the bill drops without quality quietly dropping with it.Which models do you work with, and how do you choose?
We work across Claude, GPT and Gemini, and the choice is part of the job, not a default. Some tasks want the strongest reasoning, some want speed and low cost, some need a long context window or specific tool-use behaviour. We test the realistic options against your eval harness and pick on results, not on which vendor we like. Because the prompts and evals are model-aware, switching later is a measured change, not a rewrite from scratch.Will better prompts replace fine-tuning or building features in code?
No, and we won't pretend prompting is magic. Prompt engineering gets you a long way and it's far cheaper and faster to iterate than fine-tuning, so it's the right first move for most features. But some tasks genuinely need fine-tuning, a retrieval pipeline, or plain code, and a prompt can't substitute for those. We use prompting where it's the right tool and tell you honestly when the job calls for something else, so you don't over-invest in instructions that hit a ceiling.Do you train our team or just deliver the prompts?
Both, because prompting that lives only in our heads dies the moment we leave. We deliver a versioned prompt library, the eval harness, and docs on why each prompt is shaped the way it is, then train your team to change a prompt without breaking the eval that protects it. If you want to go deeper, our AI training covers system prompts, few-shot, context engineering, RAG and evals end to end, so your team can build and measure the next feature without us.
Stop shipping prompts on vibes. Engineer them.
A 60-minute audit, your AI feature diagnosed, a plan with the evals baked in. If your team can run the prompt library in-house after setup, we'll hand you the playbook. If we're the right fit, we handle it.