Generative AI Engineering Certification

Module 1: Introduction to Generative AI Engineering

• Generative AI concepts
• AI engineering lifecycle
• Generative AI applications
• Engineering challenges
• Production considerations

Module 2: Generative AI Architecture

• Application layer
• Model layer
• Data layer
• Integration layer
• Infrastructure layer

Module 3: Large Language Model Fundamentals

• Transformer concepts
• Tokens and tokenization
• Context windows
• Model parameters
• Language generation

Module 4: Understanding Foundation Models

• Foundation model concepts
• General-purpose models
• Specialized models
• Open and hosted models
• Model capabilities

Module 5: Model Selection

• Task requirements
• Model capabilities
• Context requirements
• Latency considerations
• Cost considerations

Module 6: Generative AI APIs

• API fundamentals
• Model endpoints
• Authentication
• Requests and responses
• Error handling

Module 7: Prompt Engineering

• Prompt structure
• System instructions
• Context building
• Few-shot examples
• Prompt templates

Module 8: Advanced Prompting Techniques

• Prompt chaining
• Task decomposition
• Dynamic prompting
• Contextual prompting
• Prompt optimization

Module 9: Structured Outputs

• Structured response formats
• JSON outputs
• Schema definitions
• Response parsing
• Output validation

Module 10: Building Text Generation Applications

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

Module 11: Conversational AI Applications

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

Module 12: Context Engineering

• Context construction
• Context selection
• Managing long contexts
• Context compression
• Relevant information retrieval

Module 13: Embeddings

• Embedding concepts
• Vector representations
• Semantic similarity
• Embedding generation
• Embedding applications

Module 14: Vector Databases

• Vector storage
• Indexing
• Similarity search
• Metadata filtering
• Vector database architecture

Module 15: Retrieval-Augmented Generation

• RAG fundamentals
• Retrieval pipelines
• Context augmentation
• Grounded generation
• RAG architecture

Module 16: Document Processing for RAG

• Document ingestion
• Text extraction
• Chunking strategies
• Metadata
• Preparing searchable content

Module 17: Advanced Retrieval Strategies

• Semantic retrieval
• Hybrid retrieval
• Query transformation
• Reranking
• Retrieval optimization

Module 18: Building Knowledge-Based AI Systems

• Knowledge sources
• Knowledge ingestion
• Search pipelines
• Grounded responses
• Knowledge updates

Module 19: Tool and Function Calling

• Tool definitions
• Function selection
• Argument generation
• Tool execution
• Processing tool results

Module 20: Integrating External APIs

• Third-party services
• Database integration
• Business applications
• Data transformation
• Integration workflows

Module 21: AI Agents

• Agent concepts
• Goals and actions
• Tool-enabled agents
• Agent state
• Controlled autonomy

Module 22: Agentic Workflows

• Sequential workflows
• Conditional workflows
• Routing
• Task decomposition
• Workflow orchestration

Module 23: Multi-Agent Systems

• Specialized agents
• Agent responsibilities
• Delegation
• Agent communication
• Coordination patterns

Module 24: Memory and State Management

• Short-term context
• Persistent memory
• Session state
• Workflow state
• Memory retrieval

Module 25: Multimodal Generative AI

• Text inputs
• Image understanding
• Audio concepts
• Document inputs
• Multimodal workflows

Module 26: Image-Based AI Applications

• Image inputs
• Visual question answering
• Image information extraction
• Combining text and images
• Visual workflow design

Module 27: Generative AI Data Pipelines

• Data ingestion
• Data preprocessing
• Transformation
• Storage
• Pipeline orchestration

Module 28: Fine-Tuning Fundamentals

• Fine-tuning concepts
• Training datasets
• Supervised fine-tuning
• Parameter-efficient approaches
• Selecting when to fine-tune

Module 29: Generative AI Evaluation

• Evaluation criteria
• Accuracy and relevance
• Groundedness
• Task completion
• Evaluation datasets

Module 30: Automated Evaluation

• Evaluation pipelines
• Test datasets
• Model-based evaluation
• Regression testing
• Comparing model versions

Module 31: Human Evaluation

• Evaluation rubrics
• Human review
• Response comparison
• Reviewer consistency
• Feedback analysis

Module 32: Reducing Hallucinations

• Understanding hallucinations
• Grounding strategies
• Retrieval support
• Output validation
• Uncertainty handling

Module 33: Generative AI Security

• Access controls
• API security
• Credential protection
• Sensitive data handling
• Secure architecture

Module 34: Prompt Injection Defence

• Prompt injection concepts
• Untrusted content
• Instruction boundaries
• Tool restrictions
• Defensive design

Module 35: Guardrails

• Input guardrails
• Output guardrails
• Tool permissions
• Application boundaries
• Escalation mechanisms

Module 36: Responsible AI Engineering

• Human oversight
• Bias awareness
• Transparency
• Data considerations
• Responsible automation

Module 37: Testing Generative AI Applications

• Functional testing
• Integration testing
• Scenario testing
• Edge cases
• Regression testing

Module 38: Observability

• Application logging
• Model interactions
• Execution traces
• Error tracking
• Usage monitoring

Module 39: Performance Optimization

• Response latency
• Parallel processing
• Streaming
• Caching
• Efficient workflows

Module 40: Cost Optimization

• Token consumption
• Model routing
• Caching strategies
• Request optimization
• Cost monitoring

Module 41: Reliability Engineering

• Failure scenarios
• Retry strategies
• Timeouts
• Fallback mechanisms
• Graceful degradation

Module 42: Generative AI Application Architecture

• Monolithic applications
• Service-based architectures
• AI gateways
• Data services
• Architecture selection

Module 43: Deploying Generative AI Applications

• Environment configuration
• Application hosting
• Model connectivity
• Deployment workflows
• Production configuration

Module 44: Scaling Generative AI Systems

• Concurrent requests
• Load management
• Rate limits
• Queue-based processing
• Scaling infrastructure

Module 45: Building an Enterprise RAG Application

• Defining requirements
• Preparing knowledge sources
• Building retrieval
• Generating grounded responses
• Evaluating the application

Module 46: Building an AI Agent Application

• Defining agent objectives
• Connecting tools
• Managing state
• Implementing controls
• Testing agent behaviour

Module 47: Production Generative AI Operations

• Monitoring application quality
• Managing model changes
• Tracking failures
• Reviewing user feedback
• Continuous optimization

Module 48: Designing a Production-Ready Generative AI Solution

• Requirements analysis
• Architecture design
• Model and data strategy
• Security and reliability
• Continuous improvement