Choose your starting point
If you are new to programming, first practice Python functions, files, exceptions, virtual environments and version control. Build a small script that reads a permitted dataset and returns a checked result. If you already develop software, focus on model uncertainty, retrieval evaluation and operational cost. If you manage teams, begin with use-case selection, output verification, data boundaries and human approval before considering engineering depth.
Use the Enterprise AI school to compare specializations after you identify the responsibilities you want to practice.
Checkpoint 1: make a reproducible application
Build a small API that accepts a request, validates input and returns a structured response. Document installation, configuration and a repeatable test. Keep secrets out of source control. Be able to explain what happens when an input is missing, a dependency times out or a request is repeated.
Your evidence: a short setup guide, a request-and-response example, a test result and a diagram. A successful screenshot alone does not show how another person can reproduce the behavior.
Checkpoint 2: test a grounded answer
Retrieval-augmented generation, or RAG, supplies retrieved reference material to a model. Build with synthetic or approved documents, preserve source identifiers, and compare answers against a small question set. Include questions the documents cannot answer. Retrieved context can still be irrelevant or wrong; source references need checking.
Your evidence: the question set, expected supporting passages, observed answers, citations and failure analysis. See the RAG readiness checklist for a concrete example brief.
Checkpoint 3: add agents only for a defined reason
A fixed workflow may be enough when the steps are predictable. Consider an agent when the system needs to choose tools or routes based on the task. LangChain provides application components; LangGraph supports explicit state and routing for workflows. Learn the concepts before choosing a framework.
Your evidence: a workflow diagram, tool permissions, retry limits, a stop condition and the actions requiring human approval. Explain why the extra flexibility is worth its added evaluation and operational work. Do not assume an agent is automatically better than a simpler pipeline.
Checkpoint 4: operate within boundaries
Measure response quality alongside latency and request cost. Record model and dependency versions, keep sensitive data out of logs, and define a fallback when a dependency fails. In a sandbox, demonstrate how you stop or roll back a change. Production deployment needs a separate review of access, security, monitoring and the organization’s requirements.
Your evidence: a small results table, a runbook, a known-limitations note and a clear list of unfinished work. Explain where AI helped produce your code and how you verified it.
Turn the roadmap into a learning decision
Choose the checkpoint you cannot yet complete independently. Ask a trainer which foundations you need and how the proposed course assesses that gap. Compare Generative AI Engineer with Agentic AI Engineer; confirm the full syllabus, live schedule, assigned instructor and practice costs before enrolling.
For official implementation examples, consult the LangGraph retrieval-agent tutorial. For organizational risk framing, consult the NIST AI Risk Management Framework. This roadmap is Neriya’s educational planning example, not a certification or a production approval.
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