AI TRAINING
Building LLM Applications with LangChain
Build production-grade LLM applications using LangChain, LangGraph, and LangSmith from scratch to deployment.
What it covers
Participants learn to design and implement LLM-powered applications using LangChain's core abstractions: chains, retrievers, memory, and tool-calling agents. The programme covers stateful multi-agent workflows with LangGraph, prompt management, and RAG pipeline construction. Evaluation and observability are addressed hands-on through LangSmith tracing and automated testing. Format is a structured bootcamp mixing live coding sessions, project work, and code review.
What you'll be able to do
- Build a fully functional RAG pipeline using LangChain retrievers, vector stores, and a custom chain from a real document corpus
- Design and implement a tool-calling ReAct agent that integrates external APIs and handles multi-step reasoning
- Model a stateful multi-agent workflow using LangGraph with conditional edges, checkpointing, and human-in-the-loop steps
- Instrument an LLM application with LangSmith to capture traces, create evaluation datasets, and run automated regression tests
- Apply production patterns including streaming responses, token budget control, fallback chains, and structured output parsing
Topics covered
- LangChain core abstractions: LLMs, prompts, chains, and output parsers
- Retrieval-Augmented Generation (RAG) pipeline design and optimisation
- Memory management and conversational agents
- Tool-calling and ReAct agent patterns
- Stateful multi-agent orchestration with LangGraph
- Prompt versioning and management with LangChain Hub
- Evaluation, tracing, and dataset testing with LangSmith
- Production deployment patterns: async, streaming, and cost management
Delivery
Delivered as a 3–5 day live bootcamp (remote or on-site). Each day is split roughly 40% instruction and 60% hands-on coding on a shared capstone project. Participants receive a pre-configured dev environment (Docker or GitHub Codespaces), access to an OpenAI or Azure OpenAI API key for the duration, and a private LangSmith workspace. Async Q&A channel provided for two weeks post-bootcamp. Remote delivery uses VS Code Live Share for pair-review sessions.
What makes it work
- Participants bring a real internal use case to work on during the bootcamp, ensuring immediate applicability
- LangSmith evaluation datasets are created during training and handed off as living regression suites post-bootcamp
- A designated internal LangChain champion is identified before the bootcamp to maintain momentum and answer peer questions
- Teams pair Python developers with domain experts during agent design sessions to ground tool definitions in real workflows
Common mistakes
- Skipping LangGraph in favour of raw LangChain LCEL chains when workflows require state or branching logic, leading to brittle spaghetti callbacks
- Ignoring evaluation from day one, teams ship RAG systems without measuring retrieval precision or answer faithfulness
- Over-engineering custom chain abstractions before exhausting built-in LangChain components, causing unnecessary maintenance burden
- Hardcoding prompts as plain strings instead of using LangChain Hub, making versioning and A/B testing nearly impossible in production
When NOT to take this
A team that has not yet chosen an LLM stack and is still evaluating whether to use LangChain vs. LlamaIndex vs. raw API calls, they need an architecture decision workshop first, not a LangChain-specific bootcamp.
Providers to consider
Sources
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