WHO THIS IS FORBeginners, Python developers and AI builders
01PRIVATE KNOWLEDGE TO GROUNDED ANSWERS
The RAG Application Engineering Handbook
Build reliable Retrieval-Augmented Generation applications—from loading and chunking documents to semantic search, cited answers, evaluation, security and deployment.
Connected learning pathBeginner-friendlyReal project scenarios
Start with Byte 01 Knowledge→Retrieve→Context→Cited answer
HANDBOOK OVERVIEW
See the whole system.
Then master each decision.
This is a connected learning path—not a collection of isolated tips. Every Byte builds the mental model, makes the workflow visible and applies it to a situation you could meet in a real project.
Designed for practical understandingFinish ready to recognise, apply and explain the concept.
STARTING POINTBasic Python; no RAG experience required
02WHAT YOU WILL BUILDAn ingest → retrieve → ground → evaluate → deploy workflow
03THE MAANAVAN LEARNING METHODOne clear progression in every Byte
Move from first understanding to confident explanation.
UNDERSTANDBuild the mental modelStart with plain language and a familiar analogy.VISUALISEFollow the workflowSee how each component connects and why it matters.APPLYEnter a real scenarioUse the concept in a practical project situation.EXPLAINMake the decision clearFinish with mistakes, takeaways and an interview answer.
CONNECTED LEARNING PATH
Learn in the order the work happens.
Complete one focused concept at a time. Every chapter includes practical guidance you can use immediately.
START5 guided BytesPRACTICAL OUTCOME
BYTE 01 OF 05 · Generative AI & KnowledgeUnderstand RAG
beginner · 18 minWhat Is RAG and Why Does It Matter?
Understand how retrieval gives an AI application access to current company knowledge and produces grounded, cited answers.
AFTER THIS BYTEBuild a clear foundation
BYTE 02 OF 05 · Generative AI & KnowledgeFollow the pipeline
beginner · 20 minHow RAG Works End to End
Follow a user question through document preparation, retrieval, context assembly, grounded answer generation and traceable source citation.
AFTER THIS BYTEFollow the working process
BYTE 03 OF 05 · Generative AI & KnowledgeDiagnose failures
beginner · 22 minWhy RAG Answers Go Wrong
Diagnose stale sources, broken chunks, weak retrieval and unsupported generation using evidence before RAG application users are affected.
AFTER THIS BYTEConnect the concept to practice
BYTE 04 OF 05 · Generative AI & KnowledgeImprove quality
beginner · 22 minEvaluating and Improving RAG Quality
Measure RAG retrieval quality, groundedness, completeness and answer relevance with repeatable evaluations instead of relying on impressive demos.
AFTER THIS BYTERecognise risks and controls
BYTE 05 OF 05 · Generative AI & KnowledgeRun production
beginner · 23 minRAG in Production: Scale, Cost and Governance
Move a RAG application from pilot to production with access control, auditability, source freshness, reliability monitoring and cost visibility.
AFTER THIS BYTEApply it with confidence