GenAI Application Engineering
Build production RAG & prompt chain applications, design streaming chat UIs, implement guardrails & evaluation, optimize LLM inference costs, and deploy on Kubernetes.
Verifiable skill graph
12 skill groups · each becomes a signed node on your graph.
Verifiable skill graph
12 skill groups · each becomes a signed node on your graph.
Every lab you pass signs a W3C Verifiable Credential on your public skill graph. Completing the labs in each group below mints one node on that graph — the badge you walk away with is a cryptographic record of what you can ship, not a completion certificate.
Share the URL on your résumé or with a hiring manager. They click; they see the discipline, the labs you passed, and the verification signature. No honor system, no broker.
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.
What you'll ship in production
Core responsibilities this discipline prepares you for.
What you'll ship in production
Core responsibilities this discipline prepares you for.
- 1
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
- 2
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
- 3
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
- 4
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
- 5
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
- 6
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
- 7
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
Curriculum
8 courses · each builds on previous goals
Curriculum
8 courses · each builds on previous goals
14 goals unlocked for preview — click to read. Locked goals need a subscription.