Unified LLM API Masterclass with Praveen Kumar Moses

Stop building fragile demo wrappers. Learn how to architect, optimize, and deploy resilient, vendor-agnostic LLM applications using the modern Python SDKs: OpenAI Responses API, Anthropic Messages API with Extended Thinking, and Google GenAI unified SDK (google-genai).

This comprehensive masterclass delivers 6 complete production-ready study guides (PDFs) containing 28 hands-on engineering lessons. Every concept includes production code, line-by-line mechanics, failure modes, and interview questions.

📦 What is inside this Masterclass Bundle:

• Phase 1: Modern LLM Architecture & Core SDK Setup
  • Token mechanics, context windows, top-p, temperature, and stop sequences
  • OpenAI Python SDK v1+ & modern Responses API (client.responses.create)
  • Anthropic Python SDK: Messages API, Extended Thinking & prompt caching
  • Google GenAI SDK (google-genai): Gemini 2.0/2.5 Flash & Pro integration
  • Tri-Provider Gateway Abstraction Layer
• Phase 2: Structured Outputs & Strict Schema Enforcement
  • Why regex and naive json.loads crash production pipelines
  • OpenAI native Structured Outputs with strict Pydantic schemas
  • Anthropic constrained decoding & XML tag-enforced extraction
  • Gemini response_schema with Pydantic typing
  • Unified cross-provider polymorphic schema normalizer
• Phase 3: Function Calling, Tool Use & Model Context Protocol (MCP)
  • Next-token prediction for RPC argument generation internals
  • OpenAI parallel tool execution loops & tool result submission
  • Anthropic tool use, stop_reason == "tool_use" & MCP architectural fundamentals
  • Google GenAI manual vs. automatic callable function dispatch
  • Production Multi-Tool Agent backend (SQL read-only execution, financial math)
• Phase 4: High-Throughput Streaming & Native Multimodal Pipelines
  • Server-Sent Events (SSE) streaming mechanics across providers
  • Asynchronous token streaming with FastAPI StreamingResponse
  • Vision intelligence with OpenAI & Claude (OCR, invoices, tables)
  • Native multimodal processing with Gemini (audio, video, 1,000+ page PDFs)
  • Real-time partial JSON streaming & tool call parsing (jiter)
• Phase 5: Production Infrastructure, Caching & High Availability
  • HTTP 429 mitigation with jittered exponential backoff & fallback cascades
  • Provider prompt caching (Anthropic cache breakpoints, Gemini context cache, OpenAI automatic prefix cache)
  • Precise token ledger accounting (tracking thinking/reasoning vs completion cost)
  • Semantic caching with Redis & vector embeddings for sub-15ms hits
  • Distributed tracing with OpenTelemetry (OTel) GenAI semantic conventions
• Phase 6: Enterprise Security, Guardrails & System Design
  • Prompt injection, indirect injection fencing, canary tokens & dual-LLM evaluators
  • Real-time PII anonymization & token masking with Microsoft Presidio
  • 99.99% SLA Multi-LLM Gateway architecture & Staff/Principal AI Engineer system design rubrics

🎯 Who is this for?

• Backend & AI Engineers building enterprise GenAI microservices.
• Tech Leads & Architects designing multi-provider LLM gateways.
• Developers preparing for Senior/Staff GenAI System Design interviews.