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AI Engineering

85OpenAI API: Models and Endpoints Overview
86Anthropic Claude API: Constitutional AI Approach
87Google PaLM and Gemini API Fundamentals
88Azure OpenAI Service: Enterprise Deployment
89Open Source LLM API Standards: OpenAI Compatibility
90Request-Response Pattern: Synchronous Generation
91System, User, and Assistant Message Roles
92Temperature, Top-p, and Generation Parameters
93Stop Sequences and Max Tokens Configuration
94Multi-Provider Abstraction: LiteLLM Pattern
95API Client Libraries and SDK Best Practices
96Fallback Strategies and Provider Redundancy
97API Key Management Fundamentals
98Environment Variables and Secrets
99Bearer Token Authentication
100Rate Limiting Basics
101Handling Rate Limit Errors
102Request Queuing and Throttling
103Multi-Key Rotation Strategies
104Usage Tracking and Budget Alerts
105Organization and Project-Level Keys
106Graceful Degradation Patterns
107Understanding Streaming vs Batch Responses
108Server-Sent Events (SSE) Fundamentals
109Implementing Streaming with OpenAI API
110Handling Partial Responses and Deltas
111Error Handling in Streaming Contexts
112Streaming in Web Applications
113Buffering and Display Strategies
114Cancellation and Timeout Management
115Logging and Monitoring Streaming Requests
116Streaming Function Calls and Tool Use
117Understanding API Pricing Models
118Token Counting and Cost Estimation
119Implementing Usage Tracking
120Cost Attribution and Budgeting
121Self-Hosted Infrastructure Costs
122API vs Self-Hosted Break-Even Analysis
123Hybrid Deployment Strategies
124Cost Monitoring and Alerting

AI Engineering

85OpenAI API: Models and Endpoints Overview
86Anthropic Claude API: Constitutional AI Approach
87Google PaLM and Gemini API Fundamentals
88Azure OpenAI Service: Enterprise Deployment
89Open Source LLM API Standards: OpenAI Compatibility
90Request-Response Pattern: Synchronous Generation
91System, User, and Assistant Message Roles
92Temperature, Top-p, and Generation Parameters
93Stop Sequences and Max Tokens Configuration
94Multi-Provider Abstraction: LiteLLM Pattern
95API Client Libraries and SDK Best Practices
96Fallback Strategies and Provider Redundancy
97API Key Management Fundamentals
98Environment Variables and Secrets
99Bearer Token Authentication
100Rate Limiting Basics
101Handling Rate Limit Errors
102Request Queuing and Throttling
103Multi-Key Rotation Strategies
104Usage Tracking and Budget Alerts
105Organization and Project-Level Keys
106Graceful Degradation Patterns
107Understanding Streaming vs Batch Responses
108Server-Sent Events (SSE) Fundamentals
109Implementing Streaming with OpenAI API
110Handling Partial Responses and Deltas
111Error Handling in Streaming Contexts
112Streaming in Web Applications
113Buffering and Display Strategies
114Cancellation and Timeout Management
115Logging and Monitoring Streaming Requests
116Streaming Function Calls and Tool Use
117Understanding API Pricing Models
118Token Counting and Cost Estimation
119Implementing Usage Tracking
120Cost Attribution and Budgeting
121Self-Hosted Infrastructure Costs
122API vs Self-Hosted Break-Even Analysis
123Hybrid Deployment Strategies
124Cost Monitoring and Alerting

← AI Engineering

Lesson 94 of 1,886·3. API-First AI EngineeringPro lesson

Multi-Provider Abstraction: LiteLLM Pattern

Use LiteLLM and similar tools to write provider-agnostic code that works across OpenAI, Anthropic, and others.

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