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GenAI Application Engineering

L4-L5 · 7 courses · 114 chapters

Build production RAG & prompt chain applications, design streaming chat UIs, implement guardrails & evaluation, optimize LLM inference costs, and deploy on Kubernetes.

What you'll learn

Core responsibilities this discipline prepares you for.

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

Your learning path

7 courses · sequenced for compounding · 114 chapters

Beginner13 Ch

Foundations

Python Essentials for Agent Builders

Master Python fundamentals from zero to professional code structure. Builds incrementally toward agent-ready patterns.

Intermediate20 Ch

Step 2

LLM Foundations for Agent Builders

Deep understanding of LLM internals, data pipelines, architecture, and multi-provider integration patterns.

Intermediate17 Ch

Step 3

Kubernetes Essentials for GenAI Engineers

Ship GenAI workloads on K8s — pods, services, Helm, GPU scheduling, and production-grade deployment patterns.

Intermediate12 Ch

Step 4

Web APIs & Services for GenAI Engineers

Design, build, and harden HTTP APIs with FastAPI — auth, streaming, rate limiting, OpenAPI contracts.

Advanced15 Ch

Step 5

GenAI Inference Engineering

Production-grade LLM application development with hosted APIs (Anthropic, OpenAI, Gemini) — retries, fallbacks, caching.

Advanced16 Ch

Step 6

GenAI Agent Engineering

Build production-grade agents with hosted LLMs — planning, tools, memory, evaluation, and orchestration patterns.

Advanced21 Ch

Capstone

Full-Stack GenAI Applications

End-to-end GenAI apps — Next.js frontend, FastAPI backend, vector DB, auth, billing, and production deploy.

GenAI stack that you will run labs

Tools and APIs you invoke directly from every lab in this discipline — not the infrastructure GenBodha uses to host them.

OpenAI API

Call GPT-4o for chat, tools, and embeddings

Anthropic API

Claude 4 Opus / Sonnet for quality-critical calls

Gemini API

Gemini 2.5 Pro for multimodal labs

LangChain

Compose prompt chains and tool calling

FastAPI

Build streaming chat APIs in labs

Next.js

Build the frontend your GenAI app ships with

pgvector

Vector search in Postgres for RAG labs

Redis

Semantic cache + session memory

Langfuse

Trace every LLM call in your labs

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