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BarrioVibe
Software & AI

AI Prompt Engineering

Teams that adopt AI without a system get inconsistent results, because everybody prompts differently and nobody measures the difference. We build tested prompt libraries for the tasks you actually repeat, score them against your own examples, and train the team on them. Output quality stops depending on who happened to write the prompt that day.

Start a projectWhat’s included

Scope

What'sincluded

Everything below is in the standard engagement. Anything outside it is agreed in writing before the work starts, never after.

  • An audit of how your team uses AI today and where the output breaks down
  • A prompt library for your recurring tasks, versioned and documented
  • System prompts and reusable templates for your own tools and products
  • Evaluation sets, so a prompt change is measured rather than argued about
  • Model selection advice weighed against cost, speed and quality
  • Team training and written usage guidelines

Who this is for

Built for three situations

  • Teams with inconsistent AI output
  • Content and support operations
  • Product teams shipping AI features

Process

How thisactually runs

  1. Audit

    Current usage and outputs reviewed to identify where results are unreliable and what that costs in rework.

  2. Build

    Prompts written, structured and tested against real examples from your own work, not synthetic ones.

  3. Evaluate

    Outputs scored against a written rubric, so an improvement can be shown rather than claimed.

  4. Train and document

    The team trained on the library and the guidelines, with one named person responsible for keeping it current.

Deliverables

What you end up holding

  • A documented, versioned prompt library
  • System prompts and templates ready to use
  • An evaluation set with scored baseline results
  • Written AI usage guidelines for your team
  • A recorded training session

Next step

Tell us your situation and we will scope it.

Talk to us about it

Scope, fee and dates confirmed in writing before anything starts.

Questions

AboutAI Prompt Engineering

  • Is prompt engineering still a real discipline?

    The parlour tricks are gone. Current models do not need coaxing with magic phrases, and anyone still selling those is behind. What is left is the part that always mattered more: giving the model the right context, specifying the task precisely, and building an evaluation so you can tell whether a change actually helped. Better models made that work more valuable rather than less.

  • Can you not just tell us the prompts?

    A prompt with no evaluation set behind it is a guess that happened to work once. What you are buying is the system around it: a library tied to your real tasks, scored against your own examples, with guidelines for extending it when the task changes. A list of prompts on its own stops working the moment the task shifts slightly, and you would have no way of noticing.

  • Which models does this cover?

    Whichever your team uses, whether Claude, ChatGPT, Gemini or a model inside your own product. Structure transfers across models better than specific phrasing does, which is exactly why we document the structure and the evaluation rather than just the text.

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