Flagship project
RAG-Powered Q&A Bot
Deploy a RAG system that answers questions from a custom knowledge base.
- RAG
- Vector DB
- LangChain
RAG BootcampHigh-Growth Skill
Complete Retrieval-Augmented Generation (RAG) Bootcamp in Amritsar – Zero to Job Ready with Real Projects & Placement Support
Retrieval-Augmented Generation (RAG) Bootcamp Course course in AmritsarJob-oriented RAG training built on real industry scenarios — small batches, daily hands-on labs and 100% placement assistance.
3 / 6 MonthsBeginnerHybrid
3 / 6 Months
Duration
Weekday/Weekend
Mode
12th Pass/Any Grad
Eligibility
10-12 Students
Batch Size
Admissions open
Retrieval-Augmented Generation (RAG) Bootcamp Course · 3 / 6 Months
Batch timings
- Morning
- Evening
- Weekend
- Live online
About the programme
Course overview
Course overviewtechcadd's Complete RAG Bootcamp Course in Amritsar is a practical, project-first introduction to Retrieval-Augmented Generation for beginners and career switchers.
It opens with AI fundamentals, LLM architecture, and prompt design principles, then moves into vector databases, semantic search, RAG pipelines, AI agents, and workflow automation on real datasets.
You finish with a documented RAG portfolio, a capstone project, CV preparation and interview coaching.
Every business in Amritsar — from retail shops near Hall Bazaar to IT companies on Airport Road, from local startups to D2C brands scaling across India — is trying to figure out how to use AI. The problem isn’t access to the technology; it’s knowing how to make it use *your* data. Generic AI models like ChatGPT, Claude, and Gemini are powerful, but they don’t know your company’s internal documents, your customer FAQs, or your latest product specs. The real skill isn’t just using AI — it’s building systems that connect AI to private, real-time data. That’s Retrieval-Augmented Generation (RAG). It’s the ability to build an AI system that retrieves relevant information from your own knowledge base and generates precise, context-aware answers. Companies are desperate for people who can build these bridges between their private data and powerful AI models, saving thousands of hours and unlocking new levels of productivity.
techcadd's Complete RAG Bootcamp Course in Amritsar is built on the projects you actually see in job descriptions and client briefs — vector database management, semantic search, RAG pipelines, and LLM integration — not abstract theory. You work with real AI models and real datasets from the very first module and finish with a documented RAG portfolio, not just a certificate. The programme runs in a 3-month foundation track, with an optional 6-month extended track for deeper mastery. Batches are capped at 10–12 students, and every project you build is reviewed weekly by mentors who still work on live AI problems.
Placement assistanceLive AI labsInternship certificate
- Duration
- 3 / 6 Months
- Level
- Beginner
- Mode
- Hybrid
- Batches
- Morning, evening & weekend
Module by module
The curriculum, module by module
Module by module curriculumThe Complete RAG Bootcamp is a nested ladder — the longer track includes every module from the shorter track and adds more depth. No module is repeated; you continue building on the skills you have already learned.
- Module 01 of 06
AI Fundamentals & Prompt Design Principles
The foundational knowledge every RAG engineer assumes you already know — how LLMs work and how to write clean, effective prompts.
Topics covered
- AI/ML basics
- LLM architecture
- tokens
- context windows
- zero-shot
- few-shot
- chain-of-thought
- role prompting
- prompt templates
- ethical considerations
Tools in this module
ChatGPTClaudeGeminiGoogle AI StudioOpenAI PlaygroundYou finish withA documented prompt portfolio with foundational prompting exercises
- Module 02 of 06
Vector Databases & RAG Pipelines
Core RAG — building AI systems that can answer questions based on your own private data.
Topics covered
- Embeddings
- vector databases
- semantic search
- chunking strategies
- RAG pipelines
- LangChain
- LlamaIndex
- document Q&A
Tools in this module
PineconeChromaDBFAISSLangChainLlamaIndexOpenAI EmbeddingsYou finish withA RAG-powered chatbot that answers questions from a set of documents
- Module 03 of 06
Capstone Project, Career & Interview Readiness
A capstone project combining RAG pipeline design, API integration, and business insights, plus career readiness for RAG engineering roles.
Topics covered
- End-to-end RAG project
- problem framing
- AI solution design
- portfolio development
- resume
- LinkedIn profile
- interview readiness
- RAG certification exam preparation
Tools in this module
PythonOpenAI APIGitHubPortfolioResumeLinkedInMock interviewsYou finish withA complete capstone project with documentation, and an interview-ready RAG portfolio and resume
- Module 04 of 06
Advanced RAG & AI Agents
AI agents and automation — creating autonomous systems that can perform complex tasks and automating business workflows.
Topics covered
- AI agents
- tool use
- multi-agent systems
- AutoGPT
- BabyAGI
- workflow automation
- advanced RAG patterns
- evaluation metrics
Tools in this module
LangChain AgentsCrewAIAutoGenZapierMakeYou finish withAn AI agent that automates a multi-step business process using a RAG pipeline
- Module 05 of 06
Fine-Tuning & Model Optimization
Fine-tuning and optimization — adapting open-source models for specialized tasks and improving RAG performance.
Topics covered
- Fine-tuning basics
- LoRA
- QLoRA
- model evaluation
- PEFT
- Hugging Face
Tools in this module
Hugging FacePEFTGoogle ColabJupyterYou finish withA report on fine-tuning an open-source model for a specific domain
- Module 06 of 06
Advanced Capstone, Career & Interview Readiness
An advanced capstone project combining RAG, AI agents, automation, and business impact, plus career readiness for senior RAG engineering roles.
Topics covered
- Full AI project lifecycle
- problem framing
- AI strategy
- system deployment
- portfolio development
- resume
- LinkedIn profile
- interview readiness
- RAG certification exam preparation
Tools in this module
GitHubPortfolioResumeLinkedInMock interviewsCase studiesYou finish withA complete advanced capstone project with documentation, and an interview-ready RAG portfolio and resume
What you build
Projects that answer the interview question for you
Portfolio projectsEach project is reviewed individually, and the review notes go into your portfolio documentation.
AI Agent for Workflow Automation
Build an AI agent that automates a multi-step business process.
- AI Agents
- Automation
- API
Fine-Tuned Model for Domain-Specific Tasks
Fine-tune an open-source model for a specialized task and evaluate its performance.
- Fine-Tuning
- Hugging Face
- PEFT
Expert Capstone Project
Complete an end-to-end RAG project with deployment, monitoring, and business impact.
- Multi-Domain
- Portfolio
- Deployment
The working loop
Learn it. Build it. Make it yours.
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 professional AI teams use it, with the workflow and standards a RAG engineer expects.
RAG-Powered Q&A Bot
- 02
Real datasets and projects
Every module runs on real AI models with real business questions and real outcomes, not a slide or a screenshot.
AI Agent for Workflow Automation
- 03
Mentor-reviewed deliverables
Each module ends with a working project reviewed weekly by a mentor who still works on live AI problems.
Fine-Tuned Model for Domain-Specific Tasks
- 04
Real projects and scenarios from the start
Every RAG pipeline is built on real-world data briefs and measured through accuracy, efficiency, and business impact, the way AI teams work.
Expert Capstone Project
The honest pitch
Your months should build more than a certificate.
Your months should build more than a certificate.A course is worth the time you give it only if you finish with real projects you can show and RAG pipelines 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 Retrieval-Augmented Generation (RAG) Bootcamp Course
Why students choose techcadd
Why students choose techcaddWhat makes this course at techcadd different from a typical alternative is that every module is built on a real RAG project and documented to professional standards, so you finish with an end-to-end AI portfolio rather than disconnected certificates.
- 01
Mentors who still work on live AI problems
Not full-time lecturers — trainers who are still doing the job they teach.
- 02
One project, extended through every module
You start the project in Module 01, extend it through every module, and complete the capstone by Module 03 or 06.
- 03
Small batch sizes
10–12 students per batch ensures every student gets personalised feedback on every deliverable.
- 04
Nested, no-repeat curriculum
Longer programs include everything from shorter tracks and add more depth — you never repeat a module.
- 05
Documented internship experience
Every student receives a documented internship certificate alongside their course certificate.
- 06
Placement support
Resume and LinkedIn preparation, interview coaching, and active placement assistance for graduates.
Eligibility
Who can join the Retrieval-Augmented Generation (RAG) Bootcamp Course course
Who can join the Retrieval-Augmented Generation (RAG) Bootcamp Course courseThis 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 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 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 Retrieval-Augmented Generation (RAG) Bootcamp Course
Tools and technologies coveredEvery 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
LLM
- ChatGPT
- Claude
- Gemini
LLM API
- OpenAI API
- Anthropic API
Programming language
- Python
LLM framework
- LangChain
- LlamaIndex
Vector database
- Pinecone
- ChromaDB
- FAISS
AI community
- Hugging Face
Multi-agent framework
- CrewAI
- AutoGen
Automation
- Zapier
- Make
Version control
- Git
Code hosting
- GitHub
Code editor
- VS Code
Notebook environment
- Jupyter
Cloud notebooks
- Google Colab
Certification
Get certified in Retrieval-Augmented Generation (RAG) Bootcamp Course
Get certified in Retrieval-Augmented Generation (RAG) Bootcamp CourseIssued 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
Future scope
Where this takes you over the next five years
Where this takes you over the next five yearsThe skill does not stand still, and neither does what it is worth.
- RAG is the new baselineEmployers increasingly assume analytical knowledge will extend to AI communication and automation.
- Automation and AI are the new layersTeams expect RAG engineers to integrate AI agents, automation, and privacy-first AI practices, not just write prompts.
The objective is practical knowledge rather than memorised concepts.
Due diligence
How techcadd compares
How techcadd comparesWhat actually differs between this course at techcadd and a typical alternative.
| What you should ask | techcadd Amritsar | Other institutes |
|---|---|---|
| Live project work | Every module, from week one | Varies, often only at the end |
| Learning format | One project extended through every module | Topics demonstrated separately, rarely combined |
| Tools used | ChatGPT, Claude, LangChain, Python | Usually limited to basic ChatGPT |
| Batch size | 10–12 students per batch | Often 25–40 students per batch |
| RAG & AI Agents | Live integration, real projects | Rarely covered |
| Advanced topics | Fine-tuning, MLOps, Automation | Often only theory |
| Automation & Scripts | AI pipelines, Zapier, Make | Rarely covered |
| Portfolio | 6 documented project deliverables | Often just assignments, not portfolio-ready work |
| Certificate | Completion certificate + documented internship | Only a completion certificate |
| Placement support | Resume, LinkedIn, interview coaching, active placement | Often only a completion certificate |
Every comparison here is about substance, not marketing language.
Before you enrol
Questions we get asked at the admissions desk
Frequently asked questionsIf something here is not covered, the Amritsar desk will answer it directly — no call-back queue.
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.
