Retrieval-Augmented Generation
Develop the skills to build a cited document question-answering pipeline through a learning path centred on Retrieval-Augmented Generation. Explore concepts, guided practice and an applied project.
Build practical Retrieval-Augmented Generation skills.
Skills and knowledge developed in this course.
- Document ingestion
- Retrieval design
- Grounded generation
- RAG evaluation
- Applied project: Build a cited document question-answering pipeline
- Retrieval-Augmented Generation — validation and review
About this course
This course develops practical capability in Retrieval-Augmented Generation. Its course-specific content covers Document ingestion, Retrieval design, Grounded generation, RAG evaluation, followed by an applied project focused on build a cited document question-answering pipeline. 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 Retrieval-Augmented Generation 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 cited document question-answering pipeline
Deliver build a cited document question-answering pipeline. 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.
01Document ingestion
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- Parsing, chunking and metadata
- Access-aware document preparation
Practical exerciseApply document ingestion to the build a cited document question-answering pipeline and capture the validation evidence.
02Retrieval design
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- Embeddings and vector search
- Keyword-hybrid search and reranking
Practical exerciseApply retrieval design to the build a cited document question-answering pipeline and capture the validation evidence.
03Grounded generation
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- Context assembly and source references
- Abstention and unsupported-answer handling
Practical exerciseApply grounded generation to the build a cited document question-answering pipeline and capture the validation evidence.
04RAG evaluation
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- Retrieval relevance and answer faithfulness
- Versioned datasets and access-control tests
Practical exerciseApply rag evaluation to the build a cited document question-answering pipeline and capture the validation evidence.
05Applied project: Build a cited document question-answering pipeline
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- Define inputs, assumptions and acceptance criteria for: Build a cited document question-answering pipeline.
- Implement the project using document ingestion, retrieval design, grounded generation, rag evaluation.
Practical exerciseBuild a cited document question-answering pipeline.
06Retrieval-Augmented Generation — 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
Document ingestion · Retrieval design
Establish the Retrieval-Augmented Generation scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Grounded generation · RAG evaluation
Apply the core Retrieval-Augmented Generation 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 cited document question-answering pipeline. - 04Learning milestone 4 In progress
Deliver the applied project
Complete build a cited document question-answering pipeline, 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 ↗