This course contains the use of artificial intelligence.
What if your AI application could do more than generate answers? What if it could decide which model should handle a task, whether a tool call is safe, and when a human needs to step in?
Welcome to Jev Fundamentals: Decision AI for Beginners, a hands-on introduction to Decision AI, AI agents, and intelligent automation. You’ll explore how Jev by TypeSafe supports structured decisions inside real-world software, and learn to combine those decisions with clear business rules, confidence thresholds, and human oversight.
Rather than watching disconnected demos, you’ll build an evolving case study called BrightDesk DecisionOps. Starting with a simple customer-support classifier, you’ll progress toward an AI decision engine that can route requests, evaluate risk, check evidence, and support safer agent workflows.
What You’ll Build and Learn
You’ll begin with Python, the Jev API, and core decision concepts such as classification, scoring, and probability-based decisions. Then you’ll learn how to design useful decision questions, interpret model outputs, set confidence thresholds, and handle uncertain results without treating every prediction as fact.
Next, you’ll explore LLM routing and AI model selection. Build workflows that choose between smaller and larger large language models (LLMs) based on task type, cost, and complexity. Integrate decision logic into LangChain and LangGraph, using structured application state, conditional branches, and human-in-the-loop approvals.
You’ll also tackle a critical challenge in agentic AI: deciding when an agent should be allowed to act. Build tool-call gating and AI guardrails that flag risky actions while enforcing permissions through deterministic code. Investigate prompt injection detection, AI safety, hallucination detection, citation verification, and retrieval-augmented generation (RAG) evidence checks.
How do you know whether a decision model is actually improving your system? You’ll create an AI benchmarking workflow that compares Jev with LLM classifiers and rule-based baselines, measuring accuracy, precision, recall, F1 score, latency, and inference cost. You’ll learn why evaluation datasets, false positives, and confidence calibration matter more than flashy performance claims.
Finally, we’ll cover production AI engineering: model version pinning, API integration, fallback strategies, observability, audit logging, enterprise data security, privacy, and AI governance. Your capstone brings these skills together in a practical, reviewable Decision AI application.
Who Should Take This Course?
This course is designed for beginners, Python developers, AI engineers, software architects, automation builders, and professionals exploring generative AI or enterprise agent systems. Basic Python familiarity helps; prior Jev experience isn’t required.
Most examples use approachable code and synthetic business data. Optional local models can support comparisons, while live Jev exercises require access to TypeSafe’s service. Because frameworks evolve, you’ll also learn patterns that remain useful when SDKs and APIs change.
You will leave with reusable project ideas for customer support automation, enterprise copilots, document review, and intelligent workflow orchestration. Whether you want to improve existing AI agents or prototype new applications, this course gives you a foundation.
By the end, you won’t just know the terminology. You’ll have hands-on experience building AI systems that can route intelligently, verify evidence, measure performance, and escalate uncertainty—instead of blindly trusting every model response.
