LLM APIs Masterclass: OpenAI, Claude, Gemini & Local Models
Coming soon BeginnerIntermediateAdvanced
Get notified on YouTube Udemy certificate: coming soon
- Udemy
- Coming soon
- Lessons
- 26
- Planned length
- About 8 h 50 min
- Projects
- 5
- Practicals
- Planned: a hands-on practical and a Udemy assignment in every section, plus a capstone
What you will be able to do
- Make your first API call and read the response
- Call Gemini with a free key
- Know what changes when your company uses a cloud provider
- Pick a provider from measured quality, cost and latency
- Keep a conversation going in code
- Ship a streaming chat app
Syllabus
Working With LLM APIs: Providers
- OpenAI API From Zero Make your first API call and read the response
- Gemini API From Zero (Free Tier) Call Gemini with a free key
- Enterprise Clouds: Azure OpenAI, Bedrock, Vertex Know what changes when your company uses a cloud provider
- Provider Shoot-Out on Your Own Task Pick a provider from measured quality, cost and latency
Working With LLM APIs: Core API Skills
- Messages, Roles and Multi-Turn Chat Keep a conversation going in code
- Streaming Responses and a ChatGPT-Style UI Ship a streaming chat app
- Tool Calling in Code Let the model call your Python functions
- Structured Output: JSON Schema, Pydantic, Invoice Extractor Get validated objects from every call
- Measure and Cut Cost and Latency Halve cost on a real feature with measurements
- Prompt Caching Cut repeat-context cost with provider caching
- Rate Limits, Fallbacks and Portable Code: LiteLLM, OpenRouter Keep an app running when a provider fails and swap models by config
Working With LLM APIs: LLM Projects
- Project: Document Summariser Summarise any PDF into key points
- Project: Cheap-First Model Router Route easy questions to a small model and hard ones to a big one
Working With LLM APIs: Ship an AI App
- Next.js Front End for Your LLM Backend Connect a Next.js page to a Python LLM backend with typed requests
- Streaming Chat UI: Tokens, Stop and Retry Stream tokens into a chat UI with stop, retry and error states
- Add Login and Per-User Limits to Your AI App Add sign-in and a daily per-user request limit so nobody burns your API budget
- Deploy Your AI App on Free Tiers, No Card Needed Put the front end and backend online on free tiers with secrets kept safe
- Project: Ship a Public AI Study Helper Launch a logged-in, streaming AI study helper at a public URL
LLM Engineering and Production: Deployment
- Wrap Your AI App in an API Serve an LLM feature over HTTP
- Dockerise an AI App Build and run your app in a container
- Deploy: HF Spaces, Render, Cloud Run, Lambda, Modal Put an app online on a free tier and scale to zero
- CI/CD for AI Apps Test, evaluate and deploy on every push
- Self-Host an Open Model on a GPU Cloud (and When Kubernetes Helps) Serve your own model with vLLM
- Capstone: Ship a Production AI Service Deploy an evaluated, traced, secured AI service
Frontier and Advanced: Reasoning Models and Inference
- Reasoning Models in Production: When They Pay Off Decide per task whether a reasoning model is worth it
- Thinking Budgets: Cost, Latency and Quality Tune reasoning effort against an eval set (verify price)
Take before
Lesson list from the ADITEJUS curriculum. Planned lengths are estimates per lesson; Udemy shows the final length.