Prompt quality is performance engineering
The same model can produce radically different outcomes depending on structure and constraints.
BUNKROS AI Training
Design prompts that produce reliable, high-signal outputs at scale.
Go beyond templates. Build robust prompt systems with context, constraints, evaluation loops, and role-specific workflows.
Why This Matters
Strategic relevance before tactical execution.
Ad hoc prompts do not scale
Teams need standardized prompt frameworks for consistency and quality assurance.
Evaluation is part of prompting
Without explicit success criteria, iteration becomes random and costly.
What You Will Learn
Practical capabilities you can apply immediately.
- Structure prompts for intent, context, constraints, and output format.
- Use system prompts and role conditioning for stability.
- Create reusable prompt libraries by task type.
- Apply evaluation rubrics to score output quality consistently.
- Use chain-of-thought alternatives responsibly with safety constraints.
- Build team workflows for prompt versioning and improvement.
Curriculum Modules
A structured path from foundations to implementation.
Module 1: Prompt Architecture Basics
Design prompts that reduce ambiguity and improve control.
Module 2: Context and Constraint Engineering
Inject relevant information while minimizing noise and drift.
Module 3: Output Reliability Techniques
Enforce format, style, and decision boundaries.
Module 4: Evaluation and Iteration Frameworks
Measure quality and optimize prompts with repeatable loops.
Module 5: Team Prompt Operations
Version, document, and maintain prompts across teams.
Module 6: Prompt Safety and Policy Alignment
Embed guardrails for compliance and risk-sensitive domains.
30-Minute Training
One focused sprint to move from theory to repeatable execution.
00:00 - 05:00
Introduction
Define the problem this track solves, pick one real workflow, and set a measurable target for the session.
05:00 - 11:00
Theory Block 1
Map the core principles so your decisions are based on system behavior, not trial-and-error prompting.
11:00 - 17:00
Exercise Block 1
Run a controlled build task with explicit constraints, then measure output quality against your rubric.
17:00 - 23:00
Theory Block 2
Add governance, validation, and failure modes so the workflow remains usable in production.
23:00 - 30:00
Exercise Block 2 + Check
Refine your first build, run a quick knowledge check, and prepare your next learning sprint.
Theory Blocks
Foundations that keep your outputs reliable.
Prompt quality is performance engineering
The same model can produce radically different outcomes depending on structure and constraints.
Ad hoc prompts do not scale
Teams need standardized prompt frameworks for consistency and quality assurance.
Evaluation is part of prompting
Without explicit success criteria, iteration becomes random and costly.
Hands-On Exercises
Short builds designed for immediate skill transfer.
Exercise 1: Module 1: Prompt Architecture Basics
Design prompts that reduce ambiguity and improve control.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Exercise 2: Module 2: Context and Constraint Engineering
Inject relevant information while minimizing noise and drift.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Exercise 3: Module 3: Output Reliability Techniques
Enforce format, style, and decision boundaries.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Knowledge Check
Validate comprehension before scaling the workflow.
Open Resources
Continue learning with high-quality public material.
Glossary
Key terms you should be fluent in for this track.
Prompt Contract
A fixed instruction pattern for role, context, constraints, and output schema.
Evaluation Rubric
A scoring framework used to compare output quality consistently.
Tools Covered
Tooling choices tied to workflow outcomes.
ChatGPT
Claude
Gemini
Promptfoo
Notion
Airtable
Zapier
LangSmith
Who This Is For
Built for operators, builders, and strategic teams.
- Professionals using AI daily who need higher output quality.
- Teams building prompt-driven internal workflows.
- Consultants delivering AI-enabled services.
- Creators and operators who want repeatable AI results.
Outcomes and Career Impact
Execution outcomes with direct professional value.
Outcome
Increase output quality and consistency across key workflows.
Outcome
Reduce rework by defining clear prompt and evaluation standards.
Outcome
Create a team prompt library ready for production use.
Outcome
Train teams to iterate prompts with measurable quality gains.
Signals from Practice
Operator-level feedback and implementation sentiment.
"This course made prompting feel like engineering, not guesswork."
"Our team now uses one shared prompt system across departments."
Access Models
Free, cohort, and enterprise pathways.
Starter
EUR 0
Prompt framework cheat sheet and quality rubric.
Pro Cohort
EUR 399
4-week lab with guided prompt reviews.
Enterprise
Custom
Prompt operations rollout and team enablement.
Ready to Start
Upgrade from prompt hacks to prompt systems.
Build reliable prompt workflows your entire team can operate.
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