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Move beyond assisted coding and learn to design, build, evaluate and operate AI-native applications that behave reliably in production.
Across 12 weekends, you move through guided foundations, applied builds, review checkpoints and a production-ready AI application with retrieval, evaluation, guardrails and operational evidence.
Share four details. We’ll capture your interest and unlock the full programme plan for review.
Continue through the outcomes, curriculum and evidence journey below.
Explore the curriculum✓ understand the problem
✓ build the workflow
✓ evaluate the output
✓ explain the evidence
This page uses the complete programme architecture of the Forward Deployed Engineer reference while adapting every section to AI-Native Developer.
AI-Native Developer is designed around decisions, repeatable practice and working artifacts—not passive tool coverage.
Individual tasks are linked so research, building, testing and review become one coherent delivery path.
Plausible output is treated as a starting point. You practise checking sources, assumptions, quality, risk and edge cases.
Every milestone includes the reasoning behind the result: what changed, what failed, what was measured and what you would improve.
Not only a list of topics covered. A chain of artifacts showing how you understand, build, evaluate, improve and communicate.
A portfolio-ready ai application architecture artifact with decisions, outputs and review evidence.
A portfolio-ready llm apis & orchestration artifact with decisions, outputs and review evidence.
A portfolio-ready retrieval & vector systems artifact with decisions, outputs and review evidence.
A portfolio-ready evaluation & safety artifact with decisions, outputs and review evidence.
A portfolio-ready production delivery & observability artifact with decisions, outputs and review evidence.
A portfolio-ready ai product capstone artifact with decisions, outputs and review evidence.
Four phases connect guided learning, applied work, evaluation and portfolio evidence without turning the page into an unstructured module list.
Learn the concepts, tools and decisions behind ai application architecture, then apply them to a reviewed artifact within AI-Native Developer. The phase is paced to make each artifact useful to the phase that follows.
Learn the concepts, tools and decisions behind llm apis & orchestration, then apply them to a reviewed artifact within AI-Native Developer. The phase is paced to make each artifact useful to the phase that follows.
Learn the concepts, tools and decisions behind retrieval & vector systems, then apply them to a reviewed artifact within AI-Native Developer. The phase is paced to make each artifact useful to the phase that follows.
Learn the concepts, tools and decisions behind evaluation & safety, then apply them to a reviewed artifact within AI-Native Developer. The phase is paced to make each artifact useful to the phase that follows.
Preparation creates fluency. Live sessions focus on implementation and judgment. Review turns activity into evidence.
“The objective is not to watch an expert work. It is to build, explain and improve your own artifact.”
Readiness & blockersClear setup and conceptual gaps.
Live implementationBuild from an empty state, decision by decision.
Learner build sprintExtend the artifact with guided support.
Failure drillDiagnose an intentionally broken output.
Reasoning clinicExplain choices and trade-offs.
Integrated buildConnect the week’s second artifact.
Judgment labRespond to ambiguity, quality or risk.
Review & next gateCapture feedback and the next improvement.
Each track arrives when the next artifact needs it. Use the tabs to inspect all six capability layers.
Learn the concepts, tools and decisions behind ai application architecture, then apply them to a reviewed artifact within AI-Native Developer.
Learn the concepts, tools and decisions behind llm apis & orchestration, then apply them to a reviewed artifact within AI-Native Developer.
Learn the concepts, tools and decisions behind retrieval & vector systems, then apply them to a reviewed artifact within AI-Native Developer.
Learn the concepts, tools and decisions behind evaluation & safety, then apply them to a reviewed artifact within AI-Native Developer.
Learn the concepts, tools and decisions behind production delivery & observability, then apply them to a reviewed artifact within AI-Native Developer.
Learn the concepts, tools and decisions behind ai product capstone, then apply them to a reviewed artifact within AI-Native Developer.
Decision Labs create controlled ambiguity around quality, evidence, scope, risk and stakeholder pressure—so the programme tests reasoning as well as execution.
Applied decision labs across the learning path.
Deliberately imperfect inputs that require verification and trade-offs.
Recorded reasoning explaining what you accepted, rejected and changed.
AI disclosure notes separating assistance from verified human judgment.
Programme Decision LabRespond to the pressure—not only the request.
model_output: "correct in demo"
evaluation_coverage: "18%"
fallback_path: "missing"
release_request: "ship to users"The readiness path moves from fundamentals through applied scenarios, portfolio explanation and a final panel-style review aligned to AI-Native Developer.
const review = {
goal: "show reliable capability",
ask: [
"what evidence supports this?",
"what failed and changed?",
"what would you do next?"
]
};
status: "ready to reason aloud"Applied work happens throughout the programme. These milestone artifacts receive deeper review and become the strongest evidence in your final story.
A reviewed ai application architecture brief that connects reliable AI-native application delivery from model interface to production observability to a visible decision, working output and evidence trail.
Role mapping, portfolio positioning, project explanation and structured practice are attached to the evidence as it appears—not postponed until the final week.
No job guarantee. Support improves evidence, readiness and application quality; outcomes depend on demonstrated capability, prior experience, interview performance and market conditions.
Clarify the target roles and establish the first evidence around ai application architecture.
Publish reviewed work across llm apis & orchestration and retrieval & vector systems.
Turn project decisions, trade-offs and improvements into interview- and stakeholder-ready narratives.
Present a production-ready AI application with retrieval, evaluation, guardrails and operational evidence, receive critique and convert the result into a focused next-step plan.
Review the learning rhythm, applied-work expectations and evidence standard before deciding whether this programme fits your current goals.
There is no single memory-based final exam. Your result is assembled from applied builds, milestone reviews, evaluation discipline, portfolio explanation and the final capstone.
Applied builds
Milestone reviews
Evaluation & judgement
Portfolio explanation
Capstone & defence
These percentages are sample assessment weights for the current static build. Replace them with the approved programme policy when the headless CMS is connected.
Explore the phases, curriculum tracks, milestone artifacts, learning rhythm, application support, fit criteria and assessment approach for AI-Native Developer.
A serious programme page should be explicit about audience, effort, applied work, certification and the limits of career support.
It is designed for Developers, Software engineers, Full-stack engineers. The strongest fit is someone who wants applied capability in reliable AI-native application delivery from model interface to production observability and can protect the stated weekly effort.
The page includes a guided foundation, but the exact prerequisite depends on the programme. Use the fit check so the admissions conversation starts with your current experience.
Plan for approximately 10–14 hrs / week. Applied builds and the capstone may require additional time during milestone weeks.
The current structure is Live cohort + four-week capstone. Guided preparation, live implementation, review and portfolio work are connected through the same milestone path.
Yes. The page includes applied work throughout and culminates in a production-ready AI application with retrieval, evaluation, guardrails and operational evidence.
AI tools are used as accelerators, not as substitutes for judgment. Learners are expected to verify outputs, document assumptions and explain what they accepted, rejected or changed.
The current pathway includes career track from w7. Support is attached to portfolio evidence as it becomes available.
No. Impacteers can support portfolio evidence, application readiness and interview practice, but employment depends on capability, prior experience, interview performance and market conditions.
Certification is based on applied builds, milestone reviews, evaluation quality, portfolio explanation and a final capstone review. The published policy should be updated through the CMS once finalised.
Yes. Tools and examples may evolve as the field changes. The intended outcomes, evidence gates and approved programme policies should remain version-controlled in the future CMS.
Review the complete programme guide or submit the fit check. Both paths are designed to make the decision more deliberate.