Building AI Applications with APIs Certification

Module 1: Introduction to AI Application Development

• AI-powered applications
• Generative AI capabilities
• Application architecture
• Common AI use cases
• Development workflow

Module 2: API Fundamentals

• Understanding APIs
• Requests and responses
• HTTP methods
• API endpoints
• Status codes

Module 3: Working with REST APIs

• REST principles
• Request structure
• Headers
• Parameters
• Response processing

Module 4: JSON and Structured Data

• JSON fundamentals
• Objects and arrays
• Request payloads
• Parsing responses
• Data validation

Module 5: API Authentication

• API keys
• Authentication headers
• Environment variables
• Credential management
• Protecting secrets

Module 6: Connecting to Generative AI Models

• Model APIs
• Sending model requests
• Processing responses
• Model configuration
• Selecting appropriate models

Module 7: Prompt Design for Applications

• System instructions
• User inputs
• Context
• Prompt templates
• Dynamic prompt construction

Module 8: Managing Model Parameters

• Output length
• Randomness controls
• Model configuration
• Response behaviour
• Balancing quality and cost

Module 9: Structured AI Outputs

• Defining output formats
• JSON responses
• Schema-based outputs
• Parsing generated content
• Output validation

Module 10: Building AI Text Applications

• Text generation
• Summarization
• Classification
• Information extraction
• Content transformation

Module 11: Conversation-Based Applications

• Chat interfaces
• Conversation history
• Context management
• Session handling
• Multi-turn interactions

Module 12: Streaming AI Responses

• Understanding streaming
• Incremental output
• Improving perceived responsiveness
• Handling stream events
• Managing interrupted streams

Module 13: Tool and Function Calling

• Defining application tools
• Tool selection
• Function arguments
• Executing functions
• Returning tool results

Module 14: Integrating External APIs

• Connecting third-party services
• Combining API responses
• Data transformation
• Authentication considerations
• Integration workflows

Module 15: Embeddings Fundamentals

• Understanding embeddings
• Vector representations
• Similarity
• Semantic search
• Embedding use cases

Module 16: Vector Databases and Search

• Vector storage
• Indexing
• Similarity queries
• Metadata filtering
• Retrieving relevant information

Module 17: Retrieval-Augmented Generation

• Understanding RAG
• Document retrieval
• Context augmentation
• Generating grounded responses
• Retrieval workflow design

Module 18: Building Document-Based AI Applications

• Document ingestion
• Text extraction
• Chunking strategies
• Embedding documents
• Question answering

Module 19: Multimodal AI Applications

• Text inputs
• Image inputs
• Document inputs
• Combining modalities
• Multimodal use cases

Module 20: File Processing Workflows

• File uploads
• Validating files
• Extracting information
• Passing content to models
• Managing processed results

Module 21: Designing AI Application Architecture

• Frontend layer
• Backend layer
• AI service layer
• Data layer
• Integration layer

Module 22: Managing Application State

• User sessions
• Conversation state
• Application state
• Persistent data
• State synchronization

Module 23: Error Handling

• API errors
• Invalid requests
• Authentication errors
• Model failures
• Graceful error responses

Module 24: Retry and Resilience Strategies

• Transient failures
• Retry logic
• Exponential backoff
• Timeouts
• Fallback strategies

Module 25: API Rate Limits

• Understanding rate limits
• Request throttling
• Managing concurrent requests
• Queue-based processing
• Handling limit errors

Module 26: AI Application Security

• Protecting credentials
• Input validation
• Access control
• Sensitive data handling
• Secure API communication

Module 27: Prompt Injection and Input Risks

• Understanding prompt injection
• Untrusted user input
• Instruction conflicts
• Tool access restrictions
• Defensive application design

Module 28: Testing AI Applications

• Functional testing
• Prompt testing
• API integration testing
• Edge cases
• Regression testing

Module 29: Evaluating AI Outputs

• Accuracy
• Relevance
• Consistency
• Structured output compliance
• Task completion

Module 30: Logging and Monitoring

• Request logging
• Error tracking
• Usage monitoring
• Performance metrics
• Application observability

Module 31: Cost Optimization

• Token usage
• Model selection
• Request optimization
• Caching strategies
• Controlling unnecessary calls

Module 32: Performance Optimization

• Reducing latency
• Parallel API requests
• Streaming
• Caching
• Efficient data processing

Module 33: Building an AI Assistant Application

• Defining requirements
• Creating the interface
• Connecting the model API
• Managing conversation context
• Testing interactions

Module 34: Building a RAG Application

• Preparing knowledge sources
• Creating embeddings
• Retrieving relevant content
• Generating responses
• Evaluating retrieval quality

Module 35: Deploying AI Applications

• Environment configuration
• Production credentials
• Deployment architecture
• Scaling considerations
• Production monitoring

Module 36: Designing Production-Ready AI Solutions

• Requirements analysis
• Architecture planning
• Security controls
• Reliability considerations
• Continuous improvement