Explain and apply the core concepts across 10 structured modules.
E×T Under Edunivra Pvt Ltd & 369 Tesla
KAI-110Advanced · Tamil and English
LLM, Prompt Engineering and RAG
Develop grounded LLM applications, retrieval pipelines and provider-neutral integrations.
9Weeks
10Modules
21Classes
60Topics
Live learningTamil + English
Course projectPortfolio ready
Course overview
A complete, classroom-ready learning structure.
This course is organised as a guided sequence of concept classes, instructor demonstrations, hands-on labs, debugging sessions and project reviews. Every class produces visible learning evidence.
Recommended effort108 hours
Structured class time27 hours
Course fee₹14,999
CertificateCourse certificate
Learning outcomes
What you will be able to do.
Outcomes connect the syllabus to demonstrable skills, assessment evidence and the final course project.
Complete 21 guided classes covering 60 syllabus topics.
Use professional tools, debugging methods and secure development practices appropriate to the course.
Plan, build, test and present the course project: Build a course-based AI tutor with citations and access control.
Complete syllabus
10 modules · 21 classes · 60 topics
Open any module to see its full class-by-class teaching structure and covered topics.
Theory
Guided lab
Workshop
01 AI Fundamentals3 classes · 7 topics · 5 hours
3 focused classes covering 7 curriculum topics through explanation, demonstration and guided practice.
Class 1: AI, Machine learning
AIMachine learningNeural networks
Class 2: Transformers, Tokens
TransformersTokensContext windows
Class 3: LLM limitations
LLM limitations
02 Prompt Engineering2 classes · 6 topics · 3 hours
2 focused classes covering 6 curriculum topics through explanation, demonstration and guided practice.
Class 1: System prompts, User prompts
System promptsUser promptsFew-shot prompts
Class 2: Structured instructions, Output constraints
Structured instructionsOutput constraintsPrompt testing
03 LLM Application Development2 classes · 6 topics · 3 hours
2 focused classes covering 6 curriculum topics through explanation, demonstration and guided practice.
Class 1: Chat, Summarisation
ChatSummarisationClassification
Class 2: Extraction, Question answering
ExtractionQuestion answeringContent generation
04 Structured Outputs2 classes · 6 topics · 3 hours
2 focused classes covering 6 curriculum topics through explanation, demonstration and guided practice.
Class 1: JSON, Schemas
JSONSchemasValidation
Class 2: Retry logic, Error handling
Retry logicError handlingGuardrails
05 Tool Calling2 classes · 5 topics · 3 hours
2 focused classes covering 5 curriculum topics through explanation, demonstration and guided practice.
Class 1: Tool definitions, Function schemas
Tool definitionsFunction schemasTool selection
Class 2: Permission checks, Tool-result processing
Permission checksTool-result processing
06 RAG Fundamentals2 classes · 6 topics · 3 hours
2 focused classes covering 6 curriculum topics through explanation, demonstration and guided practice.
Class 1: Knowledge bases, Document ingestion
Knowledge basesDocument ingestionText extraction
Class 2: Cleaning, Chunking
CleaningChunkingMetadata
07 Embeddings and Vector Search2 classes · 6 topics · 3 hours
2 focused classes covering 6 curriculum topics through explanation, demonstration and guided practice.
Class 1: Embeddings, Similarity
EmbeddingsSimilarityVector databases
Class 2: Filters, Hybrid search
FiltersHybrid searchReranking
08 RAG Pipeline2 classes · 6 topics · 3 hours
2 focused classes covering 6 curriculum topics through explanation, demonstration and guided practice.
Class 1: User query, Query transformation
User queryQuery transformationRetrieval
Class 2: Context assembly, Response
Context assemblyResponseCitations
09 RAG Evaluation2 classes · 6 topics · 3 hours
2 focused classes covering 6 curriculum topics through explanation, demonstration and guided practice.
Class 1: Retrieval accuracy, Answer relevance
Retrieval accuracyAnswer relevanceGroundedness
Class 2: Hallucination tests, Feedback
Hallucination testsFeedbackEvaluation dataset
10 Local LLM2 classes · 6 topics · 3 hours
2 focused classes covering 6 curriculum topics through explanation, demonstration and guided practice.
Class 1: Local deployment, Quantisation
Local deploymentQuantisationHardware
Class 2: Model loading, Privacy
Model loadingPrivacyProvider adapter
Course project
Turn the syllabus into portfolio evidence.
Build a course-based AI tutor with citations and access control.
1Plan2Build3Test4Present
Required evidence
- Requirement and architecture notes
- Working source-code repository
- Testing and security checklist
- Live demonstration and technical viva
Class structure
Every week moves from knowledge to proof.
The same repeatable cycle keeps classroom delivery consistent while giving learners enough time to understand, practise, build and review.
Learn
Concept class, visual architecture and instructor demonstration
Practice
Guided coding, exercises, code reading and debugging
Build
Feature implementation, module task and repository update
Test
Knowledge check, coding assessment and viva
Deploy
Documentation, security review and presentation
Seven-day rhythm
A balanced teaching, lab and portfolio schedule.
- Day 1 · Theory and concept class
- Day 2 · Instructor coding demonstration
- Day 3 · Student coding lab
- Day 4 · Real application development
- Day 5 · Assignment and debugging
- Day 6 · Revision, test and project review
- Day 7 · Self-learning and portfolio update
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