GenAI Application Engineering
Build production RAG & prompt chain applications, design streaming chat UIs, implement guardrails & evaluation, optimize LLM inference costs, and deploy on Kubernetes.
What a GenAI application engineer does · GenAI Application Engineering
You are the engineer who turns a model into a product people use. The work is the whole feature: a streaming chat interface that stays responsive, a RAG pipeline that returns the right passage with a citation, guardrails that catch the bad answer before the user sees it, and a deployment on Kubernetes that survives Monday morning. You spend most days in FastAPI, React and Next.js against the OpenAI, Anthropic and Gemini APIs, with pgvector and Redis underneath. The agent engineer builds systems that decide and act on their own; you build the applications those systems live inside, and you own the latency, cost and quality the customer actually experiences.
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8 courses, each building on the last. The bar shows each course's share of the curriculum. 51 lessons are free to read.
Python Essentials for Agent Builders 9.0% of the curriculum
In scope for GenAI Application Engineering: chapters 1–13 — 62 of this path’s 687 lessons. 1 free GenAI Application Engineering lesson
LLM Foundations for Agent Builders 12.7% of the curriculum
In scope for GenAI Application Engineering: chapters 1–10, 14–20 — 87 of this path’s 687 lessons. 1 free GenAI Application Engineering lesson
Kubernetes Essentials for GenAI Engineers 8.7% of the curriculum
In scope for GenAI Application Engineering: chapters 1–6, 9–12 — 60 of this path’s 687 lessons. 1 free GenAI Application Engineering lesson
Web APIs & Services for GenAI Engineers 8.7% of the curriculum
In scope for GenAI Application Engineering: chapters 1–10 — 60 of this path’s 687 lessons. 1 free GenAI Application Engineering lesson
GenAI Inference Engineering 11.1% of the curriculum
- Reasoning Models
- Test-Time Compute Strategies
- Agentic Patterns
- Context and Memory Management
- Reliability and Resilience
- Cost Optimization and Observability
- 6 of 13 chapters shownand 7 more
GenAI Agent Engineering 33.2% of the curriculum
In scope for GenAI Application Engineering: chapters 11–48 — 228 of this path’s 687 lessons.
GenAI Operations 3.5% of the curriculum
In scope for GenAI Application Engineering: chapters 10, 17, 20, 22 — 24 of this path’s 687 lessons.
Full-Stack GenAI Applications 13.1% of the curriculum
- Chat Completion API with StreamingBuild a FastAPI SSE streaming response endpointImplement an OpenAI GPT-4o streaming adapterImplement a Gemini 2.5 Flash streaming adapter with thinking budgetImplement an Anthropic Claude streaming adapterBuild a Llama 4 Maverick streaming adapter via Together.ai
- Multi-Provider LLM Gateway with LiteLLMExtract structured output with Instructor + PydanticBuild a usage logging system with token + cost capture
- Context Engineering & Conversation MemoryBuild a context window composer with token budgetsImplement Anthropic prompt caching with cache_control markers
- Message Processing PipelineBuild a code validator with Gemini ToolCodeExecution
- Feedback, Regeneration & Edit APIsBuild a Pydantic AI regeneration agent with typed tools
- File Upload & Document ProcessingBuild a document chunking pipeline (recursive, semantic, token-aware)
- Multi-Modal Input/Output APIsBuild a vision analysis API with GPT-4o + Gemini concurrentlyBuild a Gemini grounded-generation endpoint with Google SearchBuild content safety middleware with Llama Guard 4
- MCP, Tool Execution & Agentic BackendsBuild an MCP server exposing business logic as toolsBuild an MCP client in FastAPIBuild a Pydantic AI agent with typed tools and DIBuild an agentic loop executor with SSE-streamed stepsBuild a Google ADK agent with MCP + multi-agent delegation
- Real-Time Collaboration BackendBuild a WebSocket connection manager with JWT authBuild presence tracking with Redis sorted setsBuild an event broadcast system with Redis pub/sub
- Authentication, Safety & GuardrailsBuild JWT auth with refresh-token rotation and Redis sessionsBuild conversation ownership + RBAC sharingBuild Llama Guard 4 content classifier
- Data Modeling for AI ApplicationsBuild polymorphic file/attachment + embedding models
- API Gateway with Rate Limiting & GuardrailsBuild Redis-backed token-bucket rate limiterBuild gateway-level guardrails with audit loggingBuild K8s liveness/readiness probes with dependency monitoring
- Hybrid RAG Backend with Vector SearchBuild a RAG document ingestion pipeline (Crawl4AI + Unstructured)Build hybrid retrieval (semantic + BM25 + reranking)Orchestrate RAG with LlamaIndex WorkflowsBuild an agentic RAG agent with Pydantic AIEvaluate RAG quality with RAGAS metrics
- Cost Tracking, Caching & Budget EnforcementBuild a semantic cache with Redis + embedding similarity
- Testing & Evaluation for GenAI APIsBuild a streaming SSE test harness across 4 LLM providers
- Observability with Langfuse & OpenTelemetryInstrument LiteLLM calls with Langfuse tracesBuild OpenTelemetry distributed trace pipelinesManage prompt template versions with LangfuseUse Pydantic AI + Logfire as an alternative observability stack
- Performance Optimization & Load TestingBuild async connection pools with FastAPI lifespan
- Production Deployment on Cloud Run & GKEBuild multi-stage Docker images for FastAPI AI appsDeploy FastAPI to Cloud Run with auto-scalingConfigure GKE deployments with HPA on custom metricsDeploy NVIDIA NIM for self-hosted Llama 4 with LiteLLM routingDeploy MCP tool servers as sidecars with external-secrets-operator
Core responsibilities this discipline prepares you for. Open one to see how you build it.
Design and build production GenAI features (chatbots, search, summarization) into web applications
- Build streaming chat UIs with FastAPI backends using SSE and WebSocket transports
- Wire React frontends to LLM-powered APIs with end-to-end full-stack integration
- Deploy complete GenAI applications from prototype to production on Kubernetes
Implement RAG pipelines with vector databases for enterprise search and knowledge retrieval
- Build end-to-end RAG: document chunking → embedding generation → pgvector storage → LangGraph retrieval nodes
- Validate retrieval accuracy using RAGAS metrics and implement self-verification loops
- Benchmark chunking strategies and HNSW/IVFFlat index types against precision-recall tradeoffs
Optimize LLM inference for latency, cost, and reliability across multiple providers
- Configure multi-provider routing with LiteLLM gateway including load balancing and failover
- Implement semantic caching with Redis + embedding similarity to reduce costs by 40%+
- Extract structured outputs with Pydantic AI and handle provider-specific error recovery
Integrate LLM APIs (OpenAI, Gemini, Anthropic) into existing applications with error handling
- Connect to OpenAI, Anthropic, and Gemini APIs with streaming, function calling, and embeddings
- Build FastAPI rate limiting middleware with exponential backoff and retry logic
- Navigate provider contract differences across authentication, token limits, and response formats
Build GenAI agent features with tool calling, function execution, and human-in-the-loop workflows
- Design LangGraph state machines with structured tool calling and JSON schema validation
- Implement MCP tool integration for dynamic tool discovery and execution
- Wire interruptible agent workflows with human approval gates and checkpoint persistence
Evaluate model outputs using automated metrics and LLM-as-judge for production quality
- Build evaluation pipelines using RAGAS faithfulness/relevance metrics and DeepEval harnesses
- Integrate LLM-as-judge scoring into CI/CD gates for automated quality control
- Track quality metrics over time with Langfuse dashboards and regression detection
Deploy and containerize GenAI applications on Kubernetes with CI/CD
- Containerize FastAPI + LLM applications with multi-stage Docker builds
- Deploy to Kubernetes with Helm charts, readiness probes, and Ingress configuration
- Automate rollouts with ArgoCD GitOps workflows and Kustomize environment overlays
Each group becomes a signed node on your public skill graph — a recruiter-auditable record of the labs you actually passed. Share the URL; they see the discipline, the labs, and the signature.
Wire retrieval into a feature: query an existing index, assemble retrieved context under a token budget, show citations, and judge answer quality in the feature loop — consuming the index the data engineer builds, not building it.
Compose the feature's logic: multi-step prompt chains, conditional routing, map-reduce/refine, and rock-solid structured output (JSON-mode, schema-constrained generation, parse + validate + retry). Deterministic flows you author — not autonomous agent loops.
Is the feature good enough to ship? Offline eval sets, golden datasets, LLM-as-judge for feature quality, A/B tests, and regression suites that gate the merge — pre-ship, in the dev loop.
The cost/latency calls a feature author makes in app code: per-feature model selection, prompt-token reduction, response caching, retrieval-k trade-offs, and streaming for perceived latency.
The backend of a chat experience: SSE/WebSocket streaming, stop/regenerate, session and history state, and conversation memory (rolling summarization, history truncation) across multi-turn dialogue.
Keep the feature safe in the request path: input/output validation, prompt-injection defense, PII redaction, content moderation, grounding checks, and refusal/fallback UX.
The backend substrate every feature sits on: FastAPI endpoints, request/response schemas, async handling, session management, auth, and multimodal input handling.
Wire tools into a feature the developer controls: structured tool definitions, calling external APIs/DBs, and folding results into the response. Bounded calls you orchestrate — not autonomous agent loops.
See what your feature is doing: per-feature and per-conversation tracing, prompt/completion logging, user-feedback capture, and feature-level latency/error dashboards.
Ship the feature safely: app build/test/deploy pipeline, eval-in-CI merge gates, prompt-as-code versioning, and feature flags for model/prompt rollout.
Baseline LLM access in app code: provider SDK calls, auth, structured-output and streaming primitives, and multimodal inputs.
Production Python for application code: async/await, Pydantic, typing, dataclasses, and error handling.