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Course Overview and Setup

Welcome, Outcomes, and Architecture at a Glance

Environment Setup and Core Packages

Prompting and Chains

Prompt Templates and Output Parsing

LCEL: The LangChain Expression Language

Memory and Context

Conversation Memory and Summarization

Retrieval-Augmented Generation (RAG) with LangChain

Tools, Agents, and Integrations

Tooling: Functions, Tools, and Structured Calls

Agents and Multi-Step Reasoning

Evaluation, Deployment, and Scaling

Evaluation and Experimentation

Packaging, Deployment, and Observability

Hands-On Project: End-to-End LangChain App

Project Brief and Milestones

Implementation Walkthrough

Knowledge Checks

Quiz: Prompting and Parsing Basics

Quiz: LCEL and Chain Composition

Quiz: Memory and RAG

Quiz: Tools and Agents

Quiz: Evaluation and Deployment

Course Wrap-Up and Feedback

Next Steps and Further Resources

Course Survey

Build production-grade, scalable LLM applications from scratch using LangChain. In this hands-on course, you’ll learn how to design, implement, evaluate, and deploy end-to-end AI systems that combine prompts, models, memory, tools, and retrieval for reliable outcomes. We’ll start with a practical architecture overview, set up a clean Python environment, and cover the LangChain core building blocks—prompt templates, structured outputs, and the LangChain Expression Language (LCEL) for composing robust chains. You’ll master conversation memory, Retrieval-Augmented Generation (RAG), and multi-step agent workflows that call external tools safely and efficiently. Beyond prototyping, we focus on real-world constraints: observability, cost control, latency budgets, and deployment patterns with LangServe or FastAPI. Throughout, you’ll experiment, evaluate with golden sets and automatic evaluators, and iterate with LangSmith. The capstone guides you to ship a production-ready RAG assistant with memory and tools, complete with evaluation, guardrails, and deployment. Whether you’re a data scientist, MLE, or full‑stack engineer, this course gives you the patterns, code, and mental models to ship dependable LLM apps at scale.

Building Scalable End-to-End LLM Applications with LangChain

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            <p>Building Scalable End-to-End LLM Applications with LangChain equips you to move beyond demos and craft production-grade systems that are observable, testable, and cost-aware. You’ll learn where LangChain fits in the modern LLM stack—models, prompts, memory, tools, agents, and vector stores—and how to select and assemble these components into robust applications. We begin with goals, success criteria, and an architecture-at-a-glance, then set up a reliable Python environment, pin dependencies, configure API keys across providers, and add optional tracing with LangSmith for instrumentation. From the outset, you’ll see the end-to-end blueprint we’ll build throughout the course so each concept maps directly to a real application.</p>

<p>The core of the course dives into prompting and chains with practical depth: authoring PromptTemplate and ChatPromptTemplate, routing variables and partials, enforcing structured outputs with JSON schemas and Pydantic parsers, and applying guardrails and retries. You’ll master the LangChain Expression Language (LCEL) to compose prompt | model | parser pipelines, run them via invoke/stream/batch, and use branching, mapping, fallbacks, and retries—while streaming tokens to the UI and wiring observability hooks. Next, you’ll implement conversation memory (buffer, summary, combined) and learn trade-offs around token cost, latency, and drift, then build Retrieval-Augmented Generation end-to-end with loaders, chunking strategies, embeddings, vector stores (FAISS, Chroma, Elastic, PGVector), contextual compression, and rigorous evaluation.</p>

<p>With foundations in place, the course advances to tools and agents. You will define tools with decorators and JSON schemas, invoke function calls safely, manage execution timeouts and errors, and track cost. Then you’ll compare agent paradigms—ReAct, tool-calling, and plan-and-execute—configure toolkits (web search, code execution, databases), and introduce routing, guards, and stop conditions to avoid loops. A strong emphasis is placed on moving from prototype to production with predictable latency, determinism where needed, and budget controls.</p>

<p>Evaluation, deployment, and scaling round out the build lifecycle. You’ll contrast offline and online evaluation, use LangChain’s automatic evaluators (criteria, pairwise, rubric), and construct golden sets to detect hallucinations and enforce constraints. You’ll A/B test prompts, retrievers, and models with LangSmith, then package your application using LCEL, serve it via LangServe or FastAPI, manage secrets and concurrency, stream to frontends, and implement logging, tracing, and metrics with explicit cost and latency budgets. Throughout, knowledge checks reinforce learning at each stage.</p>

<p>The capstone is a hands-on project: a production-ready RAG assistant with conversation memory and tool integration. You’ll ingest data, tune a retriever, add guardrails, integrate at least two tools (such as search and a calculator or SQL), evaluate and iterate, and deploy with observability turned on. We conclude with advanced next steps—custom retrievers, adapters, multi-agent systems, model/provider trade-offs, cost optimization, and critical guidance on security, privacy, and compliance—so you leave prepared to build, evaluate, and operate scalable LLM applications in the real world.</p>
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