LangChain Development for Retrieval Augmented Generation Training Course

Course overview

Retrieval augmented generation (RAG) systems can make internal knowledge usable through natural-language applications, but prototypes often fail when documents are poorly indexed, retrieval is imprecise, prompts lack evidence controls, or production behaviour cannot be traced. This five-day LangChain Development for Retrieval Augmented Generation Training Course addresses the engineering work behind dependable RAG applications: ingesting enterprise content, selecting retrieval strategies, grounding answers in source material, evaluating quality, and managing the resulting application in a controlled environment.

Participants build RAG workflows with LangChain’s document loaders, text splitters, embeddings, vector stores, retrievers, prompt templates, runnable chains, and structured output capabilities. They compare chunking approaches, metadata filters, similarity search, MMR retrieval and reranking; implement conversational retrieval with chat history; and design citations, fallback responses and guardrails for low-confidence answers. The course also covers LangSmith tracing and dataset-based evaluation so teams can identify whether an answer failure originated in ingestion, retrieval, prompting or the model itself.

Instruction combines focused technical demonstrations with guided Python labs and engineering review sessions. Each participant develops a working RAG assistant for a defined knowledge base, such as policy documents, product manuals or support procedures. The final deliverable is a documented LangChain application blueprint containing an ingestion pipeline, retrieval configuration, prompt and response design, evaluation test set, trace-based debugging findings, and an implementation plan suitable for handover to a development or platform team.

The course is designed for software engineers, AI engineers, data professionals and technical product staff who already understand Python and want to move from isolated LLM calls to maintainable, evidence-based retrieval applications.

Course objectives

By the end of this course, participants will be able to:

• Design a LangChain RAG architecture that separates ingestion, indexing, retrieval, generation and evaluation components
• Implement document ingestion pipelines using LangChain loaders, text splitters and metadata enrichment
• Configure embedding-based retrieval with Chroma, similarity search, MMR search and metadata filtering
• Build runnable LangChain chains that combine retrievers, prompt templates, chat history and structured outputs
• Apply citation, abstention and source-grounding patterns to reduce unsupported RAG responses
• Evaluate retrieval and answer quality using LangSmith datasets, traces and targeted test cases
• Diagnose RAG failures by tracing document processing, retrieval results, prompts, model calls and outputs
• Produce a deployment-ready RAG application blueprint with architecture decisions, evaluation criteria and operating controls

Benefits of attending

Target competencies

Who should attend

• AI Engineers — who need to build and evaluate production-oriented RAG applications with LangChain
• Software Engineers — who are integrating LLM capabilities with internal documents, APIs and user-facing applications
• Machine Learning Engineers — who must select retrieval configurations and measure answer quality systematically
• Data Engineers — who prepare document pipelines, metadata and vector indexes for enterprise knowledge retrieval
• Technical Product Managers — who define grounded AI assistant requirements, acceptance criteria and risk controls
• Solutions Architects — who design scalable patterns for connecting LLM applications to organisational knowledge sources

Requirements and prerequisites

Participants should be comfortable writing and running Python scripts, using functions, dictionaries, classes and virtual environments, and calling REST APIs or SDKs. Prior exposure to LLM concepts such as tokens, embeddings, prompts and context windows is expected; participants should also understand basic JSON and command-line package installation. Experience with LangChain, vector databases or LangSmith is not required, as these are taught from first principles in the course. Deep learning theory, model training, advanced mathematics and prior MLOps experience are not required. A laptop capable of running Python development tools is essential.

Training methodology

The instructor introduces each LangChain pattern through live code walkthroughs, then participants implement it in guided Python labs using a realistic document collection. Short case reviews examine retrieval misses, misleading citations, stale content and low-confidence questions. Teams compare alternative chunking and retrieval configurations, inspect LangSmith traces, and defend their design choices against defined quality criteria. On the final day, participants consolidate their work into an application blueprint and receive instructor feedback on architecture, evaluation coverage, risk controls and practical next steps for their own environment.

Course outline

Day 1: RAG architecture and LangChain foundations

• RAG system components: ingestion, indexing, retrieval, generation and evaluation
• LangChain package structure, integrations and environment configuration
• LangChain Document objects, metadata fields and document lifecycle
• Document loaders for PDF, web, CSV and structured text sources
• Prompt templates, chat prompt messages and variable handling
• LangChain Runnable sequences, parallel execution and output parsers
• Context windows, token budgets and evidence-grounded response design

Workshop: Build a baseline LangChain question-answering chain over a small policy-document collection and record its initial answer limitations.

Day 2: Ingestion, chunking and vector indexing

• Document cleaning, normalization and metadata enrichment strategies
• RecursiveCharacterTextSplitter configuration and chunk boundary trade-offs
• Token-based splitting and structure-aware splitting for markdown and HTML
• Embedding models, semantic similarity and embedding dimension considerations
• Chroma collections, persistence and document upsert workflows
• Metadata schemas for document type, owner, date, access scope and section
• Index refresh patterns for changed, deleted and versioned source documents

Workshop: Create an ingestion pipeline that loads, cleans, chunks, embeds and indexes a supplied knowledge base in Chroma with usable metadata.

Day 3: Retrieval strategies and conversational RAG

• Similarity search parameters, score thresholds and top-k selection
• Maximum marginal relevance retrieval for diverse context selection
• Metadata filtering and self-query retrieval patterns
• Multi-query retrieval and query transformation methods
• Contextual compression and reranking concepts
• History-aware retrievers and conversational question reformulation
• Citation generation, source links and abstention response patterns

Workshop: Tune three retriever configurations against a set of user questions and produce a comparison of retrieval relevance, citations and failure cases.

Day 4: Evaluation, observability and RAG debugging

• LangSmith project setup, runs, traces and tagged experiments
• Trace inspection across loaders, retrievers, prompts, models and parsers
• Evaluation datasets with representative questions and expected evidence
• Retrieval evaluation metrics including context relevance and document recall
• Answer evaluation criteria for correctness, groundedness and completeness
• Failure taxonomy for ingestion, chunking, retrieval, prompt and model issues
• Regression testing and release gates for RAG application changes

Workshop: Use LangSmith to trace and evaluate a flawed RAG workflow, identify root causes, and document corrective changes with before-and-after evidence.

Day 5: Production design and application blueprint

• RAG architecture patterns for APIs, chat interfaces and batch knowledge workflows
• Authentication, authorization and document-level access-control considerations
• Prompt injection threats and retrieved-content safety controls
• Caching, latency budgets, model selection and cost-management techniques
• Fallback flows for missing evidence, ambiguous queries and service failures
• Configuration management for prompts, retrievers, models and evaluation datasets
• Implementation roadmap, operational ownership and acceptance criteria

Workshop: Complete and present a LangChain RAG application blueprint containing architecture, ingestion design, retrieval settings, evaluation plan, controls and delivery roadmap.

Tools & standards covered

LangChain, LangSmith, Chroma, OpenAI API

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions