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Decision context
AI prototypes can move quickly while production responsibility accumulates in evaluation, data quality, permissions, observability, failure handling, testing, and system understanding.
Dream Beyond Education
Engineering workshop
Production AI Engineering Workshop
AI prototypes can move quickly while production responsibility accumulates in evaluation, data quality, permissions, observability, failure handling, testing, and system understanding.
Education decision path
How Production AI Engineering Workshop moves from a live decision to a clearer next step
The sequence connects the program's decision context, learning outcomes, working agenda, and follow-on evidence.
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Decision context
AI prototypes can move quickly while production responsibility accumulates in evaluation, data quality, permissions, observability, failure handling, testing, and system understanding.
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Learning outcomes
This workshop turns an AI prototype into an explicit production engineering problem. It covers evaluation design, deterministic boundaries, data quality, model dependencies, observability, exception handling, human handoff, permissions, testing strategy, release discipline, and engineering evidence.
- Define evaluation criteria around the real job the AI system performs.
- Design observability for quality, failure, latency, cost, and tool use.
- Make permissions, approvals, auditability, fallback, and human handoff explicit.
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Working agenda
The session moves through a defined sequence so participants can connect the operating problem to a clearer technology decision.
- Define the production contract
- Engineer evaluation and observability
- Control authority and failure
- Preserve software longevity
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Follow-on evidence
Continuing ownership of evaluation, observability, change review, failure analysis, and production governance.
- AI Software Assurance Framework
- Software Longevity Framework
- AI Agent Development
The program clarifies decisions and next questions. Attendance, certifications, continuing-education credits, and implementation outcomes are never implied by this visual.
What participants should understand
Use the session to improve the decision before implementation begins.
This workshop turns an AI prototype into an explicit production engineering problem. It covers evaluation design, deterministic boundaries, data quality, model dependencies, observability, exception handling, human handoff, permissions, testing strategy, release discipline, and engineering evidence.
Learning outcomes
A useful session should change the questions the team asks next.
- Define evaluation criteria around the real job the AI system performs.
- Design observability for quality, failure, latency, cost, and tool use.
- Make permissions, approvals, auditability, fallback, and human handoff explicit.
- Preserve testing, architecture, documentation, and system understanding as AI increases delivery speed.
Working agenda
Move from the operating problem to a clearer technology decision.
Define the production contract
Translate the AI feature into inputs, outputs, quality expectations, boundaries, and failure modes.
Engineer evaluation and observability
Choose representative tests, quality measures, failure signals, logging, tracing, and feedback loops.
Control authority and failure
Design permissions, approvals, retries, fallback, escalation, identity, and recovery.
Preserve software longevity
Review architecture, tests, dependencies, documentation, ownership, and code-review discipline.
Supporting authority
The curriculum is connected to published Dream Beyond research and working frameworks.
AI Software Assurance Framework
The production framework behind evaluation, evidence, observability, and reliability.
ExploreSoftware Longevity Framework
A framework for keeping software understandable, maintainable, and safe to change.
ExploreAI Agent Development
Implementation capability around tools, permissions, approvals, evaluation, and human handoff.
ExploreFollow-on path
Turn learning into evidence when a real decision needs to be made.
Managed AI Reliability
Continuing ownership of evaluation, observability, change review, failure analysis, and production governance.
Explore the next stepCommon questions
Is the workshop tied to one model vendor?
No. The engineering principles apply across model providers, RAG systems, agents, and application architectures.
Does the workshop certify production readiness?
No. A readiness conclusion requires evidence from the specific system, data, architecture, controls, and operating environment.
Request the session
Design the workshop around the decision your team is facing.
Share the audience and the decision or learning goal. Dream Beyond can then determine whether this program, a different education track, or a more evidence-based assessment is the right next step.