LangChain Application Development Certification

Module 1: Introduction to LangChain

• LangChain ecosystem
• LLM application development
• Core building blocks
• Common use cases
• Application architecture

Module 2: Setting Up a LangChain Project

• Development environment
• Package installation
• Project structure
• Environment variables
• API credentials

Module 3: Working with Language Models

• Model integrations
• Chat models
• Model configuration
• Invoking models
• Processing responses

Module 4: Messages and Conversations

• System messages
• User messages
• Assistant messages
• Conversation structure
• Message handling

Module 5: Prompt Templates

• Prompt template concepts
• Dynamic variables
• Chat prompt templates
• Reusable prompts
• Prompt organization

Module 6: Advanced Prompt Design

• Context construction
• Few-shot examples
• Dynamic prompting
• Prompt composition
• Prompt optimization

Module 7: LangChain Runnables

• Runnable concepts
• Input and output flow
• Chaining operations
• Runnable composition
• Building reusable pipelines

Module 8: LangChain Expression Language

• Pipeline composition
• Sequential operations
• Data transformation
• Parallel execution
• Building application chains

Module 9: Structured Outputs

• Output schemas
• Structured model responses
• Data validation
• Parsing outputs
• Handling invalid responses

Module 10: Output Parsers

• Parser concepts
• Structured parsing
• Custom output handling
• Parsing failures
• Validation workflows

Module 11: Building Basic LLM Applications

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

Module 12: Document Loading

• Document loaders
• File-based content
• Web content concepts
• Document metadata
• Preparing source documents

Module 13: Document Transformation

• Text splitting
• Chunk size
• Chunk overlap
• Metadata preservation
• Preparing retrieval content

Module 14: Embeddings

• Embedding concepts
• Creating embeddings
• Semantic similarity
• Embedding models
• Retrieval applications

Module 15: Vector Stores

• Vector storage
• Creating indexes
• Adding documents
• Similarity search
• Metadata filtering

Module 16: Retrievers

• Retriever concepts
• Similarity retrieval
• Search configuration
• Filtering
• Selecting relevant context

Module 17: Retrieval-Augmented Generation

• RAG architecture
• Retrieval workflows
• Context augmentation
• Grounded responses
• Building RAG pipelines

Module 18: Improving RAG Quality

• Chunking optimization
• Query transformation
• Retrieval filtering
• Reranking concepts
• Evaluating retrieval quality

Module 19: Conversational RAG

• Conversation history
• Context-aware retrieval
• Follow-up questions
• Query reformulation
• Multi-turn knowledge assistants

Module 20: Tool Calling

• Tool concepts
• Defining tools
• Tool schemas
• Model tool selection
• Processing tool results

Module 21: Creating Custom Tools

• Function-based tools
• Input schemas
• External APIs
• Database operations
• Tool error handling

Module 22: AI Agents with LangChain

• Agent concepts
• Agent loops
• Tool-enabled reasoning
• Agent execution
• Controlling agent behaviour

Module 23: Agentic Workflows

• Multi-step tasks
• Task routing
• Conditional execution
• Tool orchestration
• Workflow control

Module 24: Memory and Conversation State

• Conversation history
• Session state
• Persistent information
• Context management
• State storage strategies

Module 25: Integrating External APIs

• API-based tools
• Authentication
• Request handling
• Processing API responses
• Integration error handling

Module 26: Building Knowledge Assistants

• Knowledge sources
• Document ingestion
• Retrieval pipelines
• Conversation handling
• Source-grounded answers

Module 27: Testing LangChain Applications

• Component testing
• Prompt testing
• Retrieval testing
• Agent testing
• Regression scenarios

Module 28: Evaluation

• Defining evaluation criteria
• Response quality
• Retrieval relevance
• Task completion
• Comparing application versions

Module 29: Tracing and Debugging

• Execution traces
• Inspecting chain steps
• Reviewing model interactions
• Debugging tool calls
• Identifying application failures

Module 30: Security and Guardrails

• Input validation
• Prompt injection awareness
• Tool permissions
• Sensitive information
• Application boundaries

Module 31: Performance and Cost Optimization

• Model selection
• Token efficiency
• Caching
• Latency reduction
• Usage optimization

Module 32: Deploying LangChain Applications

• Production configuration
• Environment management
• Application deployment
• Monitoring considerations
• Continuous improvement