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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