Account

GenAI Inference Engineering

L4-L5 · 7 courses · 196 chapters

Architect multi-provider LLM gateways, implement semantic caching and batch optimization, monitor provider SLAs, and optimize inference costs.

What you'll learn

Core responsibilities this discipline prepares you for.

Design LLM gateway infrastructure

routing requests across providers

Deploy and configure LiteLLM gateway on Kubernetes with provider routing rules and load balancing
Manage API key rotation, failover policies, and per-provider request distribution
Validate gateway behavior under failover scenarios and measure routing latency overhead

Optimize request latency

through caching, batching, and streaming

Implement semantic caching with Redis using embedding similarity for cache key matching
Build request batching strategies and streaming-first response patterns
Benchmark cache hit rates, measure P50/P95 latency improvements, and tune eviction policies

Implement structured output extraction

from LLMs with type safety

Use Pydantic AI for type-safe LLM interactions with guaranteed schema compliance
Build structured extraction pipelines with Instructor and DSPy for programmatic optimization
Validate extraction accuracy across providers and measure schema conformance rates

Build cost attribution and FinOps dashboards

tracking token spend

Track token costs per team, model, and feature using Langfuse cost attribution
Build Grafana dashboards for cost visualization with Prometheus budget alerting
Implement cost optimization through semantic caching, model tiering, and prompt compression

Monitor inference quality metrics

in production

Instrument LLM calls with OpenTelemetry spans capturing latency, tokens, and error rates
Set up Logfire for Python-native tracing and Prometheus for P50/P95/P99 latency monitoring
Configure alerting rules that detect latency spikes and diagnose root causes from distributed traces

Implement intelligent routing

— route queries to model tiers based on complexity

Build RouteLLM semantic routing with model cascading: cheap models for simple, expensive for complex
Configure complexity-based dispatch logic with fallback chains across providers
Demonstrate 60%+ cost savings while maintaining output quality on standardized test datasets

Manage API rate limits and quotas

across providers

Build rate limiting middleware in FastAPI with per-endpoint and per-user throttling
Configure LiteLLM quota management with per-team token budgets and key rotation policies
Validate graceful degradation behavior under sustained load with provider quota exhaustion

Deploy inference services on K8s

with scaling and health checks

Configure Kubernetes Deployments with readiness/liveness probes tailored for LLM services
Set up Horizontal Pod Autoscaler with custom metrics for token throughput scaling
Validate zero-downtime rolling updates under active inference load

Your learning path

7 courses · sequenced for compounding · 196 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.

Intermediate12 Ch

Step 3

Kubernetes Essentials for GenAI Engineers

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

Intermediate10 Ch

Step 4

Web APIs & Services for GenAI Engineers

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

Advanced18 Ch

Step 5

GenAI Inference Engineering

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

Advanced58 Ch

Step 6

Enterprise LLM Customization

Customize LLMs for enterprise — prompt engineering, RAG at scale, fine-tuning, and domain adaptation techniques.

Advanced65 Ch

Capstone

GenAI Operations

Run GenAI in production — monitoring, dunning, incident response, cost control, and the on-call runbook.

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.

LiteLLM

Unify OpenAI/Anthropic/Gemini behind one API

OpenRouter

Route across 100+ models by price and latency

OpenAI API

Baseline model for inference benchmarking

Anthropic API

Claude for quality-critical lab routes

Gemini API

Cost-tier routing labs with Gemini 2.5

Redis

Semantic caching of repeated completions

Prometheus

Collect lab-local inference metrics

Grafana

Dashboards for p50/p95 latency + token cost

FastAPI

Build your own LLM proxy in labs

PostgreSQL

Log every request for cost allocation

Langfuse

Drill into slow or failing completions

Start the GenAI Inference Engineering discipline today

7-day money-back guarantee

Subscribe — $27/mo (6-month plan) →Or save with a 4-pack bundle →