How long is the course and how are batches scheduled?

It runs across 3/6/9 months, with morning, evening and weekend batches. Working professionals usually take the weekend track; full-time students generally take the weekday morning batch.

Is this classroom training or online?

Both. The Amritsar campus runs classroom batches with lab access, and the same syllabus is delivered live online for students outside the city. Recordings are available either way for revision.

What kind of projects will I build?

14 portfolio projects, starting with python api script and building to a capstone. They are reviewed individually, and the review notes go into your portfolio documentation.

Is placement support included?

Yes. Every enrolled student gets resume and portfolio review, mock interviews with practitioners, and access to the hiring drives we run through the year. Support continues after the course ends.

Can I pay the fee in instalments?

Yes — instalment plans are available and are agreed at the counselling stage. Speak to the Amritsar admissions desk for the current structure for this programme.

Advanced Retrieval-Augmented Generation (RAG) Specialization Course in Amritsar Course

About the programme

Course overview

techcadd's Advanced Retrieval-Augmented Generation (RAG) Specialization Course in Amritsar is a lab-based, professional-level programme for learners who want to work as RAG engineers, AI engineers, or AI application developers.

It opens with Python, LLM fundamentals, and prompt engineering, then moves into RAG fundamentals, chunking strategies, embeddings, vector databases, retrieval, generation, evaluation, and advanced RAG patterns.

You finish with a professional portfolio of deployed RAG applications, a capstone project, CV preparation and interview coaching.

Retrieval-Augmented Generation (RAG) is the practice of combining large language models with external knowledge retrieval to produce accurate, grounded, and up-to-date responses. At the professional level, the job changes from writing simple prompts to owning a RAG pipeline — scoping the use case, designing chunking and embedding strategies, building vector databases, orchestrating retrieval and generation, evaluating outputs, and defending the results to a business. That is the layer this programme trains you for. Startups, IT firms, banks, healthcare providers, and manufacturers across Punjab are all adopting RAG, and each of them needs engineers who can build, evaluate and explain a RAG system before it becomes a costly mistake.

Techcadd's Advanced Retrieval-Augmented Generation (RAG) Specialization Course in Amritsar is built around the tools that appear in actual job descriptions — Python, OpenAI API, Gemini API, LangChain, Hugging Face, Pinecone, ChromaDB, and Weaviate — not academic substitutes. You work in a hands-on lab from the first week and finish with a professional portfolio of deployed RAG applications, a capstone project, and interview-ready documentation. The programme is available in 3, 6, and 9-month tracks, and each longer track includes everything from the shorter one without repeating a module. Batch sizes are capped at 10–12 students, and every deliverable is reviewed weekly by mentors who still work on live AI engagements.

Python & APIsLLM FundamentalsPrompt DesignRAG FundamentalsChunking & EmbeddingsAdvanced RAGRAG EvaluationAI Application DevelopmentDeployment & MLOpsCapstone, Certification & Placement

Inside the Amritsar lab — a walkthrough of the advanced retrieval-augmented generation (rag) specialization course in amritsar track.

Duration: 3/6/9 months
Level: Beginner to advanced
Mode: Hybrid
Batches: Morning, evening & weekend

Module by module

The curriculum, module by module

The Advanced Retrieval-Augmented Generation (RAG) Specialization Course is a nested ladder — each longer track includes every module from the shorter tracks and adds more depth. No module is repeated; you continue building on the skills you have already learned.

Module 01 of 25
Python Fundamentals
Core Python concepts — syntax, variables, data types, operators, and control flow.
Topics covered
Basics
syntax
variables
operators
Tools in this module
PythonJupyter Notebook
You finish with
A set of Python programs demonstrating core concepts
Module 02 of 25
Python Data Structures & APIs
Python data structures and API calls — lists, tuples, sets, dictionaries, and working with JSON APIs.
Topics covered
Lists
tuples
sets
dictionaries
API calls
Tools in this module
PythonRequestsJSON
You finish with
A Python script that calls an API and processes JSON data
Module 03 of 25
LLM Fundamentals
LLM fundamentals — tokens, context windows, model selection, and capabilities.
Topics covered
LLMs
tokens
context windows
model selection
Tools in this module
ChatGPTGeminiClaude
You finish with
A written comparison of LLMs and their use cases
Module 04 of 25
Prompt Design Fundamentals
Prompt design fundamentals — role prompting, few-shot prompting, and prompt structure.
Topics covered
Prompt design
role prompting
few-shot prompting
Tools in this module
ChatGPTGeminiClaude
You finish with
A portfolio of prompt designs with evaluations
Module 05 of 25
RAG Fundamentals
RAG fundamentals — architecture, use cases, and the RAG workflow.
Topics covered
Retrieval-augmented generation
architecture
use cases
Tools in this module
LangChainOpenAI API
You finish with
A documented RAG architecture and use case
Module 06 of 25
Chunking Strategies
Chunking strategies — text splitting, metadata, and chunk size optimization.
Topics covered
Chunking
text splitting
metadata
Tools in this module
LangChainPython
You finish with
A documented chunking strategy with evaluation
Module 07 of 25
Embeddings & Vector Databases
Embeddings and vector databases — embeddings, similarity search, and integration with LLMs.
Topics covered
Embeddings
vector databases
similarity search
Tools in this module
PineconeChromaDBWeaviateHugging Face
You finish with
A vector database-powered application
Module 08 of 25
Retrieval & Generation
Retrieval and generation — retrieval strategies, prompt construction, and generation with LLMs.
Topics covered
Retrieval
generation
prompt construction
Tools in this module
LangChainOpenAI APIPinecone
You finish with
A RAG pipeline with retrieval and generation
Module 09 of 25
Practitioner Capstone — RAG Application
A complete practitioner capstone — building a RAG application from scratch.
Topics covered
End-to-end RAG app build
deployment
Tools in this module
Full AI stackGitHub
You finish with
A complete RAG application and documentation
Module 10 of 25
Advanced RAG
Advanced RAG — re-ranking, hybrid search, and query expansion.
Topics covered
Advanced RAG
re-ranking
hybrid search
Tools in this module
LangChainPineconeCohere
You finish with
An advanced RAG pipeline with re-ranking and hybrid search
Module 11 of 25
RAG Evaluation & Testing
RAG evaluation and testing — metrics, A/B testing, and RAG evaluation frameworks.
Topics covered
Evaluation metrics
A/B testing
RAG evaluation
Tools in this module
LangSmithRAGASPython
You finish with
A RAG evaluation framework with test results
Module 12 of 25
Vector Databases & Advanced RAG
Vector databases and advanced RAG — embeddings, similarity search, and integration with LLMs.
Topics covered
Vector databases
embeddings
similarity search
Tools in this module
PineconeChromaDBWeaviate
You finish with
A vector database-powered application
Module 13 of 25
Deployment & Production
Deployment and production — Docker, Kubernetes, AWS, monitoring, and scaling.
Topics covered
Production deployment
monitoring
scaling
Tools in this module
DockerKubernetesAWSMLflow
You finish with
A production-deployed RAG application with monitoring
Module 14 of 25
Industry Capstone Project
A complete industry-style capstone combining prompt engineering, RAG, and deployment.
Topics covered
End-to-end RAG project
deployment
Tools in this module
Full AI stackGitHub
You finish with
A complete end-to-end capstone project and report
Module 15 of 25
AI System Design
AI system design — architecture, scalability, and designing production AI systems.
Topics covered
AI system design
architecture
scalability
Tools in this module
Architecture patternsAWSDocker
You finish with
An AI system design document with architecture
Module 16 of 25
MLOps & Model Monitoring
MLOps — experiment tracking, model monitoring, and CI/CD for ML.
Topics covered
MLflow
model monitoring
CI/CD for ML
Tools in this module
MLflowDockerGitHub Actions
You finish with
An MLOps pipeline with experiment tracking and monitoring
Module 17 of 25
Advanced GenAI
Advanced generative AI — multimodal models, image generation, and creative AI.
Topics covered
Advanced GenAI
multimodal models
Tools in this module
OpenAI APIHugging FaceStable Diffusion
You finish with
An advanced GenAI project with documentation
Module 18 of 25
AI Application Development
AI application development — APIs, deployment, and building production-ready AI apps.
Topics covered
Building AI applications
APIs
deployment
Tools in this module
FastAPIFlaskStreamlitDocker
You finish with
A deployed AI application with documentation
Module 19 of 25
RAG Portfolio
Portfolio development — GitHub, LinkedIn, and personal branding for RAG roles.
Topics covered
Portfolio
GitHub
personal brand
Tools in this module
GitHubLinkedInPortfolio
You finish with
A published RAG portfolio with projects
Module 20 of 25
Professional Capstone & Deployment
A complete professional capstone project combining all skills learned throughout the programme.
Topics covered
Professional capstone
end-to-end RAG
Tools in this module
Full AI stackGitHub
You finish with
A complete professional capstone project and report
Module 21 of 25
Advanced RAG Patterns
Advanced RAG patterns — multi-hop RAG, agentic RAG, and advanced retrieval strategies.
Topics covered
Advanced RAG patterns
multi-hop RAG
agentic RAG
Tools in this module
LangChainAutoGenCrewAI
You finish with
An advanced RAG system with documentation
Module 22 of 25
AI System Design
AI system design — architecture, scalability, and designing production AI systems.
Topics covered
AI system design
architecture
scalability
Tools in this module
Architecture patternsAWSDocker
You finish with
An AI system design document with architecture
Module 23 of 25
Cloud Deployment (AWS/GCP)
Cloud deployment for AI — AWS, GCP, Docker, and Kubernetes.
Topics covered
Cloud deployment
AWS
GCP
Tools in this module
AWSGCPDockerKubernetes
You finish with
A cloud-deployed RAG application with documentation
Module 24 of 25
Real-World AI Solutions
Real-world AI solutions — case studies, problem-solving, and industry applications.
Topics covered
Real-world AI
problem-solving
case studies
Tools in this module
Full AI stack
You finish with
A real-world RAG solution with documentation
Module 25 of 25
Advanced Capstone Project
An advanced capstone project combining all skills learned throughout the programme.
Topics covered
Advanced capstone
end-to-end RAG
Tools in this module
Full AI stackGitHub
You finish with
A complete advanced capstone project and report

What you build

Projects that answer the interview question for you

Each project is reviewed individually, and the review notes go into your portfolio documentation.

Flagship project

Python API Script

Write a Python script that calls an API and processes JSON data.

Python
Requests
JSON

02

Prompt Design Portfolio

Design a portfolio of prompts with evaluations.

ChatGPT
Gemini
Claude

03

RAG Pipeline

Build a retrieval-augmented generation pipeline with vector databases.

LangChain
Pinecone
ChromaDB

04

Chunking Strategy

Document a chunking strategy with evaluation.

LangChain
Python

05

Vector Database Application

Build a vector database-powered application with embeddings and similarity search.

Pinecone
ChromaDB
Weaviate

06

Advanced RAG Pipeline

Build an advanced RAG pipeline with re-ranking and hybrid search.

LangChain
Cohere
Pinecone

07

RAG Evaluation Framework

Build a RAG evaluation framework with test results.

LangSmith
RAGAS
Python

08

AI Application

Deploy an AI application with FastAPI or Streamlit and Docker.

FastAPI
Docker
Streamlit

09

MLOps Pipeline

Build an MLOps pipeline with MLflow and CI/CD.

MLflow
Docker
GitHub Actions

10

RAG Portfolio

Publish a RAG portfolio with projects to GitHub and LinkedIn.

GitHub
LinkedIn
Portfolio

11

End-to-End Capstone

Complete an end-to-end RAG capstone combining prompts, RAG, and deployment.

Full AI Stack
GitHub

12

Advanced RAG System

Build an advanced RAG system with multi-hop and agentic RAG.

LangChain
AutoGen
CrewAI

13

Cloud-Deployed RAG App

Deploy a RAG application to AWS or GCP with Docker and Kubernetes.

AWS
GCP
Docker
Kubernetes

14

Real-World RAG Solution

Build a real-world RAG solution with documentation.

Full AI stack

The working loop

Learn it. Build it. Make it yours.

Every project moves through the same loop: understand the brief, build it with guidance, then explain the decisions behind your work. The certificate is the receipt — the portfolio is the point.

01
Industry-focused curriculum
You learn it the way it is used on real AI projects, with the workflow and standards an AI team expects.
Python API Script
02
Live labs, not slides
Every module runs in a hands-on lab against real LLM APIs and vector databases — not screenshots.
Prompt Design Portfolio
03
Mentor-reviewed deliverables
Each module ends with a documented RAG pipeline and evaluation, reviewed weekly by a mentor who still works on live AI engagements.
RAG Pipeline
04
Reporting and certification
Every exercise ends with a written report, and the programme closes with a practical assessment and certification.
Chunking Strategy

The honest pitch

Your months should build more than a certificate.

A course is worth the time you give it only if you finish with work you can show and skills you can defend.

Project completion certificate

Issued for the project you build and defend

Course completion certificate

Carries a certificate ID and a scan-to-verify code

Why choose Advanced Retrieval-Augmented Generation (RAG) Specialization Course in Amritsar

Why students choose techcadd

What makes this course at techcadd different from a typical alternative.

01
Mentors who still ship client work
Not full-time lecturers — trainers who are still doing the job they teach.
02
Live labs, not slides
Every module runs in a hands-on lab against real LLM APIs and vector databases — not screenshots.
03
Tools used in job descriptions
Python, OpenAI API, Gemini API, LangChain, Pinecone, ChromaDB — not outdated or academic substitutes.
04
Nested ladder structure
Each longer track includes every module from the shorter one without repeating a module.
05
AI-assisted learning
You learn to use AI assistants and prompt engineering as practical tools in data analytics.

Eligibility

Who can join the Advanced Retrieval-Augmented Generation (RAG) Specialization Course in Amritsar course

This course works for a specific starting point — here's who gets the most out of it.

Educational backgroundNo specific degree or stream required — school leavers, graduates and career switchers all qualify.
Prior experienceNone required. The programme opens at beginner level and builds from there across 3/6/9 months.
AgeNo upper age limit. What matters is being able to commit to the full course rather than dropping off midway.
EquipmentA laptop or desktop with a stable internet connection for classroom or live-online batches.
Time commitmentAble to attend regularly across 3/6/9 months, in a morning, evening or weekend batch.

Where it leads

The roles this portfolio opens

Job titles vary by company, but the underlying expectations do not. Each destination below maps to work you will have already done during the programme.

Tools you will actually use

The working stack for Advanced Retrieval-Augmented Generation (RAG) Specialization Course in Amritsar

Every tool below is installed, configured and used by you during the course — not demonstrated on a slide. You leave able to set up your own environment from scratch.

20tools covered

Programming language

Python

Development environment

Jupyter Notebook

AI assistant

ChatGPT
Gemini
Claude

AI API

OpenAI API
Claude API
Gemini API

LLM framework

LangChain

Multi-agent framework

AutoGen
CrewAI

Vector database

Pinecone
ChromaDB
Weaviate

NLP/LLM

Hugging Face

Web framework

FastAPI
Flask

Deployment framework

Streamlit

Containers

Docker

Orchestration

Kubernetes

Certification

Get certified in Advanced Retrieval-Augmented Generation (RAG) Specialization Course in Amritsar

Issued on completion against the modules you finished and the projects you submitted — plus an internship letter where the industrial training track applies. Shareable to LinkedIn, and verifiable by an employer who calls the Amritsar desk.

Module-wise assessment, not attendance-based
Project submissions logged against your certificate
Internship letter on the industrial training track
Verifiable directly with the campus office

Enrol for the next batch Download brochure

Future scope

Where this takes you over the next five years

The skill does not stand still, and neither does what it is worth.

AI-assisted analyticsAI is transforming analytics, making AI-assisted detection and analysis increasingly valuable.
Self-service BISelf-service BI is becoming a standard requirement in analytics job descriptions.
Cloud analyticsCloud analytics platforms are moving from niche to standard expectations.
Data storytellingData storytelling and communication are now a dedicated requirement in analytics job descriptions.
Real-time analyticsReal-time analytics is becoming a standard requirement in analytics job descriptions.

What you walk away with

The objective is practical knowledge rather than memorised concepts.

Due diligence

How techcadd compares

What actually differs between this course at techcadd and a typical alternative.

techcadd Amritsar compared with a typical training institute in the region
What you should ask	techcadd Amritsar	Other institutes
Live analytics project work	Every course, from week one	Varies, often only at the end
Small batches (10–12)	Capped for mentor attention	Often 30+ students per batch
Tools used in job descriptions	Excel, Python, SQL, Power BI, Tableau, Pandas	Outdated or academic substitutes
Mentor-reviewed deliverables	Weekly review by working professionals	Often no review or feedback
AI-assisted learning	Built into the curriculum	Rarely included

Every comparison here is about substance, not marketing language.

Before you enrol

Questions we get asked at the admissions desk

If something here is not covered, the Amritsar desk will answer it directly — no call-back queue.

Call +91 99152 22509 Send an enquiry

No. The programme opens at beginner level and assumes no background beyond comfort with a computer. Students who already have some exposure move through the early modules faster and spend the saved time on project work.

Find your pace

Choose the right duration for you

Modules run 01 to 28 in a single sequence. Each track continues exactly where the shorter one ended, so a shorter duration costs you scope — never depth.

Most popular

Select a duration above to see exactly how far it reaches.

Which modules of the Advanced Retrieval-Augmented Generation (RAG) Specialization Course in Amritsar course each duration covers
Module	3months	6months	9months

01Basics · syntax · variablesPython Fundamentals	Included in the 3 months track	Included in the 6 months track	Included in the 9 months track
02operatorsPython Fundamentals	Included in the 3 months track	Included in the 6 months track	Included in the 9 months track
03Lists · tuples · setsPython Data Structures & APIs	Included in the 3 months track	Included in the 6 months track	Included in the 9 months track
04dictionaries · API callsPython Data Structures & APIs	Included in the 3 months track	Included in the 6 months track	Included in the 9 months track
05LLMs · tokens · context windowsLLM Fundamentals	Included in the 3 months track	Included in the 6 months track	Included in the 9 months track
06model selectionLLM Fundamentals	Included in the 3 months track	Included in the 6 months track	Included in the 9 months track
07Prompt design · role prompting · few-shot promptingPrompt Design Fundamentals	Included in the 3 months track	Included in the 6 months track	Included in the 9 months track
08Retrieval-augmented generation · architecture · use casesRAG Fundamentals	Included in the 3 months track	Included in the 6 months track	Included in the 9 months track
09Chunking · text splitting · metadataChunking Strategies	Included in the 3 months track	Included in the 6 months track	Included in the 9 months track
10Embeddings · vector databases · similarity searchEmbeddings & Vector Databases	Included in the 3 months track	Included in the 6 months track	Included in the 9 months track
11Retrieval · generation · prompt constructionRetrieval & Generation	Included in the 3 months track	Included in the 6 months track	Included in the 9 months track
12End-to-end RAG app build · deploymentPractitioner Capstone — RAG Application	Included in the 3 months track	Included in the 6 months track	Included in the 9 months track

13Advanced RAG · re-ranking · hybrid searchAdvanced RAG	Not included in the 3 months track	Included in the 6 months track	Included in the 9 months track
14Evaluation metrics · A/B testing · RAG evaluationRAG Evaluation & Testing	Not included in the 3 months track	Included in the 6 months track	Included in the 9 months track
15Vector databases · embeddings · similarity searchVector Databases & Advanced RAG	Not included in the 3 months track	Included in the 6 months track	Included in the 9 months track
16Production deployment · monitoring · scalingDeployment & Production	Not included in the 3 months track	Included in the 6 months track	Included in the 9 months track
17End-to-end RAG project · deploymentIndustry Capstone Project	Not included in the 3 months track	Included in the 6 months track	Included in the 9 months track
18AI system design · architecture · scalabilityAI System Design	Not included in the 3 months track	Included in the 6 months track	Included in the 9 months track
19MLflow · model monitoring · CI/CD for MLMLOps & Model Monitoring	Not included in the 3 months track	Included in the 6 months track	Included in the 9 months track
20Advanced GenAI · multimodal modelsAdvanced GenAI	Not included in the 3 months track	Included in the 6 months track	Included in the 9 months track
21Building AI applications · APIs · deploymentAI Application Development	Not included in the 3 months track	Included in the 6 months track	Included in the 9 months track

22Portfolio · GitHub · personal brandRAG Portfolio	Not included in the 3 months track	Not included in the 6 months track	Included in the 9 months track
23Professional capstone · end-to-end RAGProfessional Capstone & Deployment	Not included in the 3 months track	Not included in the 6 months track	Included in the 9 months track
24Advanced RAG patterns · multi-hop RAG · agentic RAGAdvanced RAG Patterns	Not included in the 3 months track	Not included in the 6 months track	Included in the 9 months track
25AI system design · architecture · scalabilityAI System Design	Not included in the 3 months track	Not included in the 6 months track	Included in the 9 months track
26Cloud deployment · AWS · GCPCloud Deployment (AWS/GCP)	Not included in the 3 months track	Not included in the 6 months track	Included in the 9 months track
27Real-world AI · problem-solving · case studiesReal-World AI Solutions	Not included in the 3 months track	Not included in the 6 months track	Included in the 9 months track
28Advanced capstone · end-to-end RAGAdvanced Capstone Project	Not included in the 3 months track	Not included in the 6 months track	Included in the 9 months track

Scroll the table sideways to compare all durations.

Every track is the same syllabus read in order, which is why extending repeats nothing: you carry on from the module you finished at, in the Amritsar lab or the live online batch.

Get started today

Not sure if Advanced Retrieval-Augmented Generation (RAG) Specialization Course in Amritsar is the right fit?

One call with a counsellor is usually enough to find out. Book a free demo class and see the Amritsar lab before you decide.

+91 99152 22509 Book a free demo

Next batch, Amritsar campus

Start the Advanced Retrieval-Augmented Generation (RAG) Specialization Course in Amritsar programme this intake.

Send your question and a counsellor will call you back about batch timings, fees, EMI options, placement record, or whether this course fits your background.

Free career counselling
No registration fee
Placement support included

Request a call back

About the Advanced Retrieval-Augmented Generation (RAG) Specialization Course in Amritsar course · 3/6/9 months

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Start building your career today.

Talk to a counsellor today. One call is usually enough to know which track fits your degree, your schedule and the job you want.

Call now+91 99152 22509

Free career counselling
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Placement support included

Advanced Retrieval-Augmented Generation (RAG) Specialization Course in Amritsar with Live Labs & Placement Support

Course overview

The curriculum, module by module

Python Fundamentals

Topics covered

Tools in this module

Python Data Structures & APIs

Topics covered

Tools in this module

LLM Fundamentals

Topics covered

Tools in this module

Prompt Design Fundamentals

Topics covered

Tools in this module

RAG Fundamentals

Topics covered

Tools in this module

Chunking Strategies

Topics covered

Tools in this module

Embeddings & Vector Databases

Topics covered

Tools in this module

Retrieval & Generation

Topics covered

Tools in this module

Practitioner Capstone — RAG Application

Topics covered

Tools in this module

Advanced RAG

Topics covered

Tools in this module

RAG Evaluation & Testing

Topics covered

Tools in this module

Vector Databases & Advanced RAG

Topics covered

Tools in this module

Deployment & Production

Topics covered

Tools in this module

Industry Capstone Project

Topics covered

Tools in this module

AI System Design

Topics covered

Tools in this module

MLOps & Model Monitoring

Topics covered

Tools in this module

Advanced GenAI

Topics covered

Tools in this module

AI Application Development

Topics covered

Tools in this module

RAG Portfolio

Topics covered

Tools in this module

Professional Capstone & Deployment

Topics covered

Tools in this module

Advanced RAG Patterns

Topics covered

Tools in this module

AI System Design

Topics covered

Tools in this module

Cloud Deployment (AWS/GCP)

Topics covered

Tools in this module

Real-World AI Solutions

Topics covered

Tools in this module

Advanced Capstone Project

Topics covered

Tools in this module

Projects that answer the interview question for you

Python API Script