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🤖 AI & Agentic AIBeginner LevelEvidence-Based Skill Profile

Hands-on Mastery in LangChain & AI Applications

Master practical AI application development with LangChain from absolute beginner to production scale: LCEL runnables, prompt templates, structured Pydantic outputs, ReAct agents, tool execution, conversational memory, vector embeddings, RAG, and production evaluation with your dedicated AI Agent coach.

35 Modules • 9 Production Projects
35 Modules
AI FearFilter Faculty

What You Will Learn

Master the fundamentals of LangChain orchestration, LCEL runnable pipelines, and ChatModel message abstractions.
Build reliable structured outputs using Pydantic schemas, validation error correction, and output parsers.
Develop custom tools with type-safe arguments, integrating APIs, databases, and secure sandboxes.
Implement autonomous ReAct reasoning-action loops with memory, tool selection, and execution tracing.
Ingest and split raw documents with recursive semantic chunking, indexing them into vector databases (Chroma/FAISS).
Assemble complete RAG pipelines with hybrid search, history-aware query reformulation, and source attribution.
Configure Human-in-the-Loop (HITL) approval gates and with_fallbacks circuit breakers for production resilience.
Evaluate RAG accuracy and faithfulness using Ragas benchmarks and instrument latency tracing with LangSmith.

Curriculum & Weekly Roadmap

35 Structured Modules

Module 1: Introduction to LangChain and AI Applications

  • What is LangChain?
  • Evolution of AI Application Development
  • Core Components of the Modern LangChain Ecosystem

Module 2: What Problem Does LangChain Solve?

  • The Limitations of Raw LLM API Calls
  • Standardizing Abstractions Across Model Providers
  • Orchestration and Composability in AI Engineering

Module 3: LLM Fundamentals for Application Developers

  • Tokenization, Context Windows, and Temperature
  • Deterministic vs Probabilistic AI Responses
  • Managing Latency, Rate Limits, and API Costs

Module 4: Models, Messages and Responses

  • Chat Models vs Text Completion Models
  • Initializing ChatOpenAI, ChatAnthropic, and Open-Source Models
  • Inspecting AIMessage, Response Metadata, and Token Usage

Module 5: Prompt Templates

  • Why Hardcoded String Prompts Fail in Production
  • ChatPromptTemplate and Message Role Templates
  • FewShotPromptTemplate and Dynamic Prompt Composition

Module 6: System, User and AI Messages

  • Message Roles and The Dialogue Stack
  • SystemMessage Engineering for Strict Behavioral Bounds
  • Multi-turn Conversation Thread Modeling

Module 7: Structured Outputs

  • The Fragility of Parsing Unstructured Natural Language
  • with_structured_output and Pydantic Model Binding
  • JSON Schema Enforcement and Validation Error Recovery

Module 8: LangChain Core Concepts

  • The Philosophy of LangChain Core
  • Decoupled Components and Swappable Architecture
  • Type Safety and Serialization Across Nodes

Module 9: Runnables and Execution Flow

  • Understanding the Runnable Protocol
  • RunnableParallel, RunnablePassthrough, and RunnableLambda
  • Asynchronous Streaming and Low-Latency Responses

Module 10: Chains and Sequential Workflows

  • LCEL and the Pipe Operator (|)
  • Linear Sequential Chains (Prompt -> Model -> Parser)
  • Branching Chains with RunnableBranch and Conditional Logic

Module 11: Building Your First LangChain Application

  • Setting Up the Development Environment and Secrets
  • End-to-End Execution of a Dynamic Summarizer & Analyzer
  • User Input Sanitization and Terminal/Web Integration

Module 12: Output Parsers and Structured Data

  • StrOutputParser, JsonOutputParser, and PydanticOutputParser
  • Fixing Output Parser Failures with OutputFixingParser
  • Custom Parsers for Domain-Specific Data Formats

Module 13: Conversation State and Memory

  • Stateless LLMs vs Stateful User Conversations
  • RunnableWithMessageHistory and BaseChatMessageHistory
  • Sliding Window Memory and Token-Buffered History Pruning

Module 14: Tools and Tool Calling

  • Giving LLMs Hands: The Mechanics of Tool Calling
  • Defining Type-Safe Tools with the @tool Decorator and Pydantic
  • Model Tool Binding (bind_tools) and Execution Handlers

Module 15: Building AI Agents with LangChain

  • Chains vs Agents: Static Workflows vs Dynamic Reasoning
  • The ReAct Framework: Reasoning + Acting in Action
  • Creating and Running a create_tool_calling_agent

Module 16: Agent Decision-Making and Tool Selection

  • Writing Tool Docstrings for Optimal Model Selection
  • Handling Multi-Tool Ambiguity and Negative Tool Calling
  • Agent Stopping Conditions and Recursion Limits

Module 17: Agent Workflows

  • Single Agent Loops vs Multi-Step Agent Workflows
  • State Machines and Transition Triggers in Agent Graphs
  • Persisting Agent Checkpoints and Resuming Paused States

Module 18: Document Loading and Processing

  • Ingestion Pipelines: Extracting Raw Text from PDFs, CSVs, and Webpages
  • LangChain Document Class and Metadata Preservation
  • Handling Large Document Corpora Without Memory Leaks

Module 19: Text Splitting and Chunking

  • Why Chunking Matters: Context Limits and Semantic Coherence
  • RecursiveCharacterTextSplitter and Separator Hierarchies
  • Chunk Size vs Chunk Overlap Trade-offs

Module 20: Embeddings and Vector Representations

  • From Words to Vectors: The Geometry of Meaning
  • Generating Embeddings with OpenAI, HuggingFace, and Local Models
  • Cosine Similarity, Dot Product, and Distance Metrics

Module 21: Vector Stores and Similarity Search

  • What is a Vector Database? (Chroma, FAISS, Pinecone)
  • Indexing Documents and Querying with similarity_search
  • Metadata Filtering for Multi-Tenant and Secure Search

Module 22: Retrieval-Augmented Generation with LangChain

  • The RAG Triad: Grounding, Context, and Generation
  • Turning Vector Stores into Runnables with as_retriever()
  • Building the Classic RAG Chain with LCEL

Module 23: Building a LangChain RAG Application

  • End-to-End Enterprise Document Q&A Implementation
  • Formatting Retrieved Documents into Grounded Context
  • Handling Missing Context and Preventing Hallucinations

Module 24: Retrieval Quality and Context Management

  • Maximum Marginal Relevance (MMR) for Diverse Results
  • Contextual Compression and LLM-Powered Reranking
  • MultiQueryRetriever and Query Transformation Strategies

Module 25: Multi-Step AI Applications

  • Architecting Complex AI Systems with Multiple Sub-Chains
  • Sequential vs Parallel Multi-Model Processing
  • Data Passing and State Aggregation Across Application Nodes

Module 26: Human-in-the-Loop AI Applications

  • Why Full Autonomy is Dangerous for High-Stakes Actions
  • Designing Human Approval Gates for Destructive Tools
  • Resuming Pipeline Execution After Human Review

Module 27: Error Handling and Reliability

  • Handling Model Downtime, Context Overflows, and Rate Limits
  • with_fallbacks for Automated Model Failover
  • with_retry and Exponential Backoff Strategies

Module 28: Guardrails and AI Safety

  • Protecting Against Prompt Injections and Jailbreaks
  • Output Guardrails: PII Redaction, Toxicity, and Schema Validation
  • Defensive System Prompt Design and Sandbox Isolation

Module 29: LangChain Application Testing

  • Unit Testing LLM Chains Without Paying for API Calls
  • Mocking Chat Models with FakeListChatModel
  • Deterministic Regression Testing for Production Workflows

Module 30: Evaluation of AI Applications

  • Why Traditional Software Testing Fails for AI
  • RAG Evaluation Metrics: Faithfulness, Answer Relevance, and Context Precision
  • LLM-as-a-Judge Evaluation Frameworks

Module 31: Logging, Monitoring and Debugging

  • Debugging Complex Chains with set_debug(True)
  • Full-Stack Tracing with LangSmith Observability
  • Tracking Latency, Tokens, and Cost per Chain Run

Module 32: Cost and Performance Optimization

  • Semantic Caching with CacheBackedEmbeddings and Redis
  • Model Cascading: Small Models First, Frontier Models as Fallback
  • Token Pruning and Context Window Economy

Module 33: Production AI Application Architecture

  • Decoupling the AI Layer from Web Frameworks (FastAPI / Next.js)
  • Asynchronous Queue Workers for High-Throughput AI Tasks
  • Production Telemetry, Alerting, and Audit Trails

Module 34: Deploying LangChain Applications

  • Containerizing LangChain Services with Docker
  • Serving LangChain with LangServe and FastAPI Endpoints
  • Secure API Key Management and Enterprise Secrets

Module 35: Build a Production-Style LangChain AI Application

  • End-to-End System Design: From Spec to Architecture
  • Implementing the Full-Stack Hybrid RAG and Agent Application
  • Production Verification, Testing, and Deployment Audit

Who This Course Is For

Aspiring AI Engineers, Software Developers, Backend Architects, and CS Students aiming to build and deploy production-grade LLM applications and autonomous agents with LangChain.

Key Skills Developed:

LangChain Core Architecture, Runnables & LCEL CompositionPrompt Engineering, Templates & Dynamic VariablesStructured Outputs with Pydantic & Type SafetyTools, Function Calling & Custom Execution HandlersAutonomous AI Agents & ReAct Reasoning LoopsDocument Loaders, Semantic Chunking & Vector StoresRetrieval-Augmented Generation (RAG) PipelinesHuman-in-the-Loop Approval Gates & Automated FallbacksObservability with LangSmith & RAGAS EvaluationProduction AI Application Deployment & FastAPI Serving

Course Faculty & Development

AI FearFilter Faculty

LangChain & Agentic AI Team

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