Skip to main content
Free AI courses. Free certificates of completion.Learn how
NeuroLearn AIFREE AI LEARNING
Start learning
Back to All Courses
IntermediateRAG & Knowledge Systems Free Verifiable Certificate

Retrieval-Augmented Generation: Build AI with Your Own Data

Build an inspectable retrieval pipeline, work with evidence, and evaluate the answers your AI system produces.

1h 16m of material
3 Modules (6 Lessons)
Free · no account needed to read
Access Fee
FREE100% Free Forever
Full Interactive Curriculum Access
In-Browser Prompt Studio & Code
Official Certification Exam
Verifiable PDF/PNG Certificate
Start Learning Now

The assessment unlocks once you have worked through the lessons.

Independent provider. Not an accredited awarding body. Not a regulated qualification.

What You Will Master

  • Design a RAG pipeline with separate source, retrieval, context, and answer-review stages
  • Prepare traceable chunks with source versions and access metadata
  • Explain BM25, dense retrieval, rank fusion, and candidate reranking
  • Build evidence packs and distinguish citation validity from claim support
  • Calculate retrieval metrics and document unsupported-query behavior
  • Test access boundaries and produce a capstone evaluation report

Skills Acquired

RAG ArchitectureDocument ChunkingHybrid RetrievalEvidence & CitationsRetrieval EvaluationAccess-Aware Context

Prerequisites

  • Basic Python: functions, lists, dictionaries, and running a script from a terminal
  • Familiarity with language-model prompts; the free prompting course is a useful starting point
  • Python 3.10 or later for the downloadable lab; no API key or paid service is required

Curriculum Syllabus

3 modules · 6 lessons · 1h 16m · one assessment

Module 1

Module 1: Evidence and Source Preparation

Define the answering contract and prepare complete, traceable source records.

1.1Designing an Evidence-Based Answering System
13 min
1.2Preparing Sources, Chunks, and Versions
13 min
Module 2

Module 2: Retrieval and Grounded Answers

Rank candidates, select context, and review citations against claims.

2.1Lexical, Dense, and Hybrid Retrieval
12 min
2.2Context Budgets and Verifiable Citations
12 min
Module 3

Module 3: Evaluation and Access Boundaries

Measure quality, test failure cases, and complete a reproducible capstone.

3.1Evaluating Retrieval and Answer Quality
13 min
3.2Access Boundaries and the Capstone Review
13 min
Course author

Md. Rajib Hawlader

Principal AI Research Scientist, NeuroLearn AI Labs

Writes and maintains the NeuroLearn AI curriculum.

Ready to Begin Retrieval-Augmented Generation: Build AI with Your Own Data?

Self-paced curriculum with free instant certificate issuance upon passing the evaluation.

Start First Lesson