LangChain
Develop the skills to build a tested retrieval-and-tool application through a learning path centred on LangChain. Explore concepts, guided practice and an applied project.
Build practical LangChain skills.
Skills and knowledge developed in this course.
- Core interfaces
- Retrieval components
- Tool integration
- Application evaluation
- Applied project: Build a tested retrieval-and-tool application
- LangChain — validation and review
About this course
This course develops practical capability in LangChain. Its course-specific content covers Core interfaces, Retrieval components, Tool integration, Application evaluation, followed by an applied project focused on build a tested retrieval-and-tool application. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with LangChain or plan to take on responsibilities in this area.
Before you start
Basic computer literacy and a foundational understanding of artificial intelligence & generative ai. Prior experience with the named product helps with the practical exercises.
Build a tested retrieval-and-tool application
Deliver build a tested retrieval-and-tool application. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01Core interfaces
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- Chat models, messages and prompt templates
- Runnable composition and structured outputs
Practical exerciseApply core interfaces to the build a tested retrieval-and-tool application and capture the validation evidence.
02Retrieval components
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- Loaders, splitters and embeddings
- Retriever configuration and metadata filters
Practical exerciseApply retrieval components to the build a tested retrieval-and-tool application and capture the validation evidence.
03Tool integration
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- Tool schemas and agent execution
- Callbacks and trace inspection
Practical exerciseApply tool integration to the build a tested retrieval-and-tool application and capture the validation evidence.
04Application evaluation
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- Mock dependencies and task datasets
- Handle timeouts, invalid outputs and access limits
Practical exerciseApply application evaluation to the build a tested retrieval-and-tool application and capture the validation evidence.
05Applied project: Build a tested retrieval-and-tool application
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- Define inputs, assumptions and acceptance criteria for: Build a tested retrieval-and-tool application.
- Implement the project using core interfaces, retrieval components, tool integration, application evaluation.
Practical exerciseBuild a tested retrieval-and-tool application.
06LangChain — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
Core interfaces · Retrieval components
Establish the LangChain scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Tool integration · Application evaluation
Apply the core LangChain techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for build a tested retrieval-and-tool application. - 04Learning milestone 4 In progress
Deliver the applied project
Complete build a tested retrieval-and-tool application, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| AI / ML Engineer | ₹10–17 LPA | ₹20–35 LPA | ₹38–62 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗