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Prompt engineering & system instructions

Give the AI a clearer job, better context and a repeatable way to produce useful output.

We design and improve prompts, system instructions and reusable AI operating templates for support, research, content, sales enablement and internal workflows. The focus is consistency, structure, boundaries and testability.

View support packagesSee two concrete deliverables
Reusable templatesStructured outputsEvaluation scenarios
Direct answer

What makes a good system prompt or AI instruction?

A useful instruction tells the model what job it is doing, which inputs matter, which sources or rules take priority, what output format is expected and how to behave when information is missing or conflicting. It should also be testable: instead of relying on one impressive example, we evaluate normal, ambiguous and adversarial inputs to see whether the instruction produces consistent behavior.

Two concrete example deliverables

What a completed prompt-design engagement can look like

These sample scopes show the full design and testing process. They are examples of possible work, not fabricated performance claims.

Deliverable A · Support instructions

Customer-support response system with policy boundaries

For a team that wants AI-generated support drafts to be more consistent, concise and aligned with approved policies.

  1. Collect representative examplesReview common support questions, desired answer style, policy-sensitive cases and examples of responses that currently fail.
  2. Define the AI roleSpecify whether the system drafts, summarizes or classifies, what it may reference and what always requires human review.
  3. Build the instruction hierarchyWrite rules for tone, policy precedence, missing information, clarifying questions, unsupported topics and prohibited assumptions.
  4. Standardize outputCreate a repeatable response structure such as answer, next action, uncertainty note and internal escalation flag.
  5. Test against edge casesRun ambiguous complaints, contradictory policy details, incomplete context, emotional language and requests outside scope.
  6. Refine and hand overTighten weak instructions and deliver a versioned prompt with usage notes and a regression test set.

Typical outputs

  • Production-ready system instruction draft
  • Support-response output template
  • Policy precedence and escalation rules
  • Clarification and uncertainty behavior
  • Evaluation set with normal and edge cases
  • Versioning / maintenance notes
Typical fit: 1–3 sessions for a focused system
Deliverable B · Research/content framework

Reusable research-to-content prompt framework

For a marketer, founder or team that needs a consistent process from brief to research synthesis to reviewed draft.

  1. Define the content workflowClarify audience, objective, required sources, brand constraints, output types and the human review step.
  2. Create the input briefBuild a structured request template so the model receives topic, angle, evidence requirements, tone and exclusions consistently.
  3. Separate research from draftingCreate distinct instructions for gathering evidence, identifying gaps and generating a draft only after the research requirements are satisfied.
  4. Design output schemasStandardize research notes, outline, final draft, citations or source notes and a claims-to-review section.
  5. Test varied topicsEvaluate vague briefs, thin evidence, competing sources, highly technical topics and requests that encourage unsupported claims.
  6. Package reusable templatesDeliver the final prompt set with variable fields, workflow order and a review checklist for repeated use.

Typical outputs

  • Structured content brief template
  • Research instruction prompt
  • Drafting / transformation prompt
  • Output structure and variable definitions
  • Claim and source review checklist
  • Example test prompts and maintenance notes
Typical fit: 3–6 sessions for a reusable framework

Scope note: prompt engineering can improve consistency but cannot eliminate model errors. Outputs should be reviewed when factual accuracy, legal compliance, financial impact, safety or brand risk matters.

Before we design

What we need from you

The strongest prompt work starts from real examples of the task, not only a description of the model.

Task objective

Explain what the AI should produce or decide and who will use the result.

Good examples

Share examples of outputs you consider useful, accurate and on-brand.

Bad examples

Show common failure patterns such as hallucination, verbosity, wrong tone or missing structure.

Rules and sources

Provide policies, reference material, brand guidance or other rules the system should respect.

Required output

Define whether you need prose, JSON, fields, a checklist, an email, a report or another structured format.

Model / platform

Tell us which AI system or platform you use because capabilities and instruction hierarchy can differ.

Scope boundaries

Prompt quality matters, but it is only one part of an AI system.

If inconsistent output is caused by weak source data, missing context, tool failures or workflow design, a prompt alone may not solve it. In those cases we can identify the wider issue and recommend the relevant chatbot, agent, integration or troubleshooting work rather than pretending one “magic prompt” will fix everything.

Related services

Put the instructions inside a complete implementation

Prompt design often sits at the center of chatbots, assistants and automated workflows.

Have a prompt that “sometimes works”?

Bring the task and failure examples. We can turn them into a testable instruction system.

Start small with one workflow or build a reusable framework across several sessions.

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