Model choice drives output quality
Different models excel at reasoning, speed, coding, or multimodal tasks. Wrong fit creates hidden cost.
BUNKROS AI Training
Choose the right model stack for each business-critical use case.
A practical decision framework across LLMs, multimodal systems, latency tiers, costs, and privacy constraints.
Why This Matters
Strategic relevance before tactical execution.
Benchmarks alone are misleading
You need scenario-specific tests and acceptance criteria, not leaderboard snapshots.
Switching costs are real
Architecture and prompt portability need to be designed before vendor lock-in happens.
What You Will Learn
Practical capabilities you can apply immediately.
- Design model evaluation rubrics tied to target outcomes.
- Compare performance across quality, latency, and token economics.
- Run side-by-side tests for text, code, image, and multimodal tasks.
- Build fallback and routing strategies across multiple models.
- Document model selection rationale for technical and non-technical stakeholders.
- Create a repeatable re-evaluation cadence as models evolve.
Curriculum Modules
A structured path from foundations to implementation.
Module 1: Evaluation Foundations
Define model scoring criteria and production acceptance thresholds.
Module 2: Capability and Constraint Mapping
Map strengths and failure modes by task category.
Module 3: Benchmark Design
Create realistic prompts, datasets, and scoring methods.
Module 4: Cost and Performance Engineering
Balance quality, speed, and budget across traffic patterns.
Module 5: Multi-Model Architecture
Route tasks intelligently and establish fallbacks for reliability.
Module 6: Decision Communication
Translate technical comparison findings into executive recommendations.
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.
Model choice drives output quality
Different models excel at reasoning, speed, coding, or multimodal tasks. Wrong fit creates hidden cost.
Benchmarks alone are misleading
You need scenario-specific tests and acceptance criteria, not leaderboard snapshots.
Switching costs are real
Architecture and prompt portability need to be designed before vendor lock-in happens.
Hands-On Exercises
Short builds designed for immediate skill transfer.
Exercise 1: Module 1: Evaluation Foundations
Define model scoring criteria and production acceptance thresholds.
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: Capability and Constraint Mapping
Map strengths and failure modes by task category.
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: Benchmark Design
Create realistic prompts, datasets, and scoring methods.
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.
Workflow Constraint
A rule that limits ambiguity and keeps output behavior stable across runs.
Quality Gate
A mandatory review checkpoint before downstream use or publication.
Tools Covered
Tooling choices tied to workflow outcomes.
OpenAI API
Anthropic
Google AI Studio
Mistral
Llama
Weights & Biases
Promptfoo
LangSmith
Who This Is For
Built for operators, builders, and strategic teams.
- Product managers selecting AI stacks.
- Engineers evaluating model performance for production.
- Consultants advising clients on AI vendor decisions.
- Innovation teams managing model procurement and risk.
Outcomes and Career Impact
Execution outcomes with direct professional value.
Outcome
Produce a model decision matrix usable across teams.
Outcome
Reduce model spend through routing and workload segmentation.
Outcome
Improve response quality via use-case-specific model assignment.
Outcome
Institutionalize a quarterly model review and replacement process.
Signals from Practice
Operator-level feedback and implementation sentiment.
"This track turned vague model debates into clear decisions."
"Our team stopped chasing hype and started using evidence."
Access Models
Free, cohort, and enterprise pathways.
Starter
EUR 0
Model comparison worksheet and benchmark starter kit.
Pro Cohort
EUR 449
4-week training with benchmark review sessions.
Enterprise
Custom
Custom model evaluation and architecture advisory.
Ready to Start
Stop guessing, start selecting models with evidence.
Join the decision lab and build a production-grade model selection framework.
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