#1 India's Top IT Training Institute

+91 9818366550 +91 8448807675 +91 8766313646

For Support : +91 8800023723

info@uncodemy.com

🔥 Most Popular in Rachi

RAG Engineering Training Course in Rachi by Uncodemy

Q: What is the duration of the RAG Engineering course in Rachi?

The program runs for 5–6 months and includes 140+ hours of learning across 90+ live sessions. Weekday and weekend batches are both available.

Q: Do I need prior Python or LLM experience?

Programming fundamentals are helpful but not mandatory — Python is taught from Module 2 onward. Familiarity with basic LLM concepts is a plus but built up in Module 3. A laptop with stable internet is required.

Q: What tools are covered in this course?

15+ tools including Python, OpenAI APIs, Hugging Face, LangChain, LlamaIndex, LangGraph, FAISS, ChromaDB, Pinecone, PostgreSQL/pgvector, Elasticsearch/OpenSearch, FastAPI, Docker, Git/GitHub and cloud deployment tools.

Q: Does the course cover vector databases and hybrid search?

Yes. Module 7 covers Vector Databases & Indexing (10 hours) and Module 10 covers Hybrid Search & Knowledge Retrieval (10 hours). Both are core components with hands-on exercises.

Q: What is the capstone project?

The capstone is Module 13: Enterprise RAG Projects & Capstone (20 hours). You build an end-to-end RAG application applying document processing, embeddings, vector search, retrieval, evaluation and deployment.

Q: Are the classes live or pre-recorded?

All 90+ sessions are live and instructor-led, with recordings available afterwards for revision.

Q: What is the fee for the RAG Engineering course in Rachi?

The program fee is ₹17,500 plus 18% GST. Total fee is ₹20,650. The same fee applies in all modes and cities.

Q: Are EMI options available?

EMI starts from ₹4,032 per month. The exact amount, tenure and eligibility depend on the applicable payment or financing option — a counsellor can confirm your plan.

Q: Is there a demo or trial class before I enrol?

Yes. You can attend a demo session to see the teaching style, meet the trainer and review the curriculum before deciding. Contact us to book a demo.

Q: Do you provide placement assistance?

We provide Dedicated Career & Placement Assistance — resume building, LinkedIn optimisation, portfolio review, interview preparation, mock interviews and opportunity sharing through our network of 850+ companies. We do not guarantee employment.

Rachi-based learners can enroll in Uncodemy's 5–6 month instructor-led live online RAG Engineering program. The curriculum spans document ingestion & chunking, embeddings, vector databases, retrieval pipelines, hybrid search and production RAG deployment. Tailored for developers, data scientists/ML engineers and aspiring AI engineers, the program offers 13 modules, 90+ live sessions, 140+ hours and an enterprise RAG capstone. Fee: ₹17,500 + 18% GST, total ₹20,650. EMI starting from ₹4,032/month.

4.7/5 (500+)

5/5 (30+)

4.7/5 (500+)

5/5

4/5 (130+) Trustindex

5/5 (54+)

⚡SPECIAL LIMITED TIME OFFER

Special Offer: RAG Engineering Program

Now Pay Only

₹17,500/-

+ 18% GST

No Hidden Charges

100% Transparent

Total ₹20,650

Incl. 18% GST

EMI AVAILABLE

Starting ₹4,032/M*

Fill Your Details

🔒 Your information is safe and secure.

Expert Trainers

Learn from 8+ years experienced professionals

Hands-on Automation Projects

Build real Playwright test suites, not just theory

Lifetime Access

Get lifetime access to recorded sessions & materials

Dedicated Career & Placement Assistance

Resume Building, Mock Interviews & Job Support

Certified Course

Certificate of Completion from Uncodemy

Flexible Batches

Weekday & Weekend Online & Offline

Why Choose
Uncodemy?

14+

Years of Training Excellence

850+

Companies in Our Hiring Network

54,000+

Learners Trained

200+

Corporate Associations

100%

Placement Support

Online & Offline Classes

Weekend & Weekday Batches

100% Practical Training

Affordable Fees

EMI Options Available

Dedicated Support

Program Details

RAG Engineering Course — Live Online Program Hands-on learning focused on practical RAG engineering skills and interview preparation.

A comprehensive 5–6 month instructor-led live online program covering embeddings, vector databases, retrieval pipelines, hybrid search, and production RAG deployment with practical projects, mentoring and career support.

📚 Learning:

140+ Hours of Learning

⏱️ Program Duration:

5–6 Months

🎓 Live Training:

90+ Live Sessions

🛠️ Technology Coverage:

15+ Tools & Technologies

💼 Practical Learning:

Hands-on RAG Projects & Enterprise AI Case Studies

💰 Program Fee:

₹17,500 + 18% GST

💰 Total Fee:

₹20,650

💳 EMI:

Starting From ₹4,032/Month*

🚀 Career Support:

Dedicated Career & Placement Assistance

🏢 Hiring Network:

850+ Companies in Our Hiring Network

🎯 Career Support Includes:

Resume Building & LinkedIn Optimization, Mock Interviews & Interview Preparation, Career Guidance & Job Opportunity Assistance

RAG Engineering Course in Rachi — Quick Facts

🎓 Course:

RAG Engineering

🏢 Provider:

Uncodemy Edutech Pvt. Ltd.

📚 Total Learning:

140+ Hours of Learning

📖 Curriculum:

13 Modules

💼 Projects:

Hands-on RAG Projects + Enterprise RAG Capstone

🛠️ Tools:

Python, OpenAI APIs, LangChain, LlamaIndex, Vector Databases

🌐 Modes:

Live Online (offline only at Delhi & Noida centres; Rachi learners join live online)

✅ Prior Coding:

Programming fundamentals helpful; Python taught from Module 2 onward. Basic LLM concepts built up in Module 3.

📜 Certificate:

Uncodemy RAG Engineering Program Certificate (verifiable online)

🗓️ Last Reviewed:

25 September 2026

List of Placed Students Trained by Uncodemy

By completing Uncodemy’s RAG Engineering training, Ranchi learners gain hands-on skills covering document processing, embeddings, vector database usage, retrieval pipelines and hybrid search. With training and career preparation completed, eligible learners may explore roles such as RAG Engineer, AI Application Engineer and GenAI Engineer.Placement records for Rachi learners are updated regularly — view verified outcomes at uncodemy.com/placement.

What is RAG Engineering?

RAG Engineering (Retrieval-Augmented Generation) is the practice of merging large language models with external knowledge retrieval to ensure responses are grounded in actual, current enterprise data instead of depending solely on the model's training. It encompasses document ingestion, chunking, embeddings, vector databases and retrieval pipelines that supply relevant context to an LLM at query time — lowering hallucination and empowering AI systems to answer questions about private, domain-specific or rapidly changing information.

What does a RAG engineer do? A RAG engineer manages document ingestion and chunking, constructs embeddings and maintains vector databases, designs retrieval pipelines, implements hybrid search, evaluates RAG accuracy, and deploys production RAG systems. Daily tasks include Python scripting, working with frameworks like LangChain and LlamaIndex, fine-tuning chunking and retrieval strategies, measuring relevance and faithfulness, and exposing RAG-powered APIs that applications can consume.

RAG Engineering vs LLM Engineering vs Generative AI

RAG Engineering concentrates on developing retrieval-grounded AI solutions using enterprise information, whereas LLM Engineering addresses LLM-powered applications across multiple use cases. Generative AI, on the other hand, places stronger emphasis on autonomous AI agents.

Aspect	RAG Engineering	LLM Engineering	Generative AI
Core focus	Building retrieval-grounded AI systems on enterprise knowledge	Building, fine-tuning & deploying LLM-powered applications broadly	Building GenAI apps + autonomous AI agents
Coding depth	High	High	Moderate to High
Typical entry salary	₹5–9 LPA (Entry-Level RAG Engineer)	₹6–10 LPA (per LLM Engineering course sheet)	[CONFIRM per GenAI course sheet]
Uncodemy program duration	5–6 months	5–6 months	~3–4 months (indicative)

Salary ranges are indicative and vary by role, skills, experience, location and employer.

Course Overview

The Uncodemy RAG Engineering course provides a 5–6 month live online learning experience through 90+ instructor-led sessions and 140+ hours of training. Its 13 modules cover the complete progression from RAG and enterprise knowledge AI foundations to Python for RAG Engineering, LLM fundamentals and API integration, document processing and data ingestion, text chunking and document transformation, embeddings and semantic search, vector databases and indexing, RAG, advanced retrieval and reranking, hybrid search and knowledge retrieval, RAG evaluation and optimization, production RAG APIs and deployment, and a 20-hour enterprise RAG capstone. Learners get practical exposure through exercises using real tools in each module and finish the program with hands-on RAG projects and an enterprise capstone. The training is conducted fully live online for Ranchi learners and students throughout India.

Who should join this course:

Developers wanting to specialise in retrieval-grounded AI systems and enterprise knowledge assistants.
Data scientists and ML engineers upskilling into RAG engineering and vector search.
Aspiring AI engineers looking to build production-ready RAG applications.
Working professionals moving into GenAI/RAG roles from software or analytics backgrounds.
Rachi-based learners who want instructor-led live online training with hands-on projects.

Eligibility: Learners from Ranchi do not require prior programming experience to enroll in the RAG Engineering course. Although understanding programming basics can be beneficial, Python is taught from Module 2 onwards. Familiarity with introductory LLM concepts is useful but not essential because the course builds this knowledge in Module 3. A laptop and reliable internet connection are required to complete the practical training activities.

Upcoming RAG Engineering Course Batches for Rachi Learners

For learners in Ranchi, Uncodemy offers live online batches with flexible IST-based schedules. You can choose between weekday and weekend sessions depending on your work or study commitments. The program is designed to combine convenient timings with practical, hands-on learning. Attend a free demo class first-hand or select an upcoming batch that suits your schedule.

Weekdays

Date	Time	Trainer	Seats Left
16oct	07:30 PM – 08:30 PM	Mr. Irshad	01Seat
22oct	02:00 PM – 03:00 PM	Mr. Upendra Kumar Tiwari	02Seats
26oct	10:30 AM – 11:30 AM	Mr. Irshad	01Seat
30oct	12:00 PM – 01:00 PM	Mr. Upendra Kumar Tiwari	02Seats

Weekends

Date	Time	Trainer	Seats Left
27oct	03:00 PM – 05:00 PM	Mr. Irshad	02Seats

8+ new batches start every month. All timings are in IST. Seats are limited and updated daily.

Enroll Now

Curriculum for RAG Engineering Training Course in Rachi

RAG Engineering Course Syllabus in Rachi

The syllabus is organised into 13 modules covering 140 hours. The order follows how RAG engineering actually works: foundations → Python → LLM fundamentals & API integration → document processing & chunking → embeddings & semantic search → vector databases → RAG → advanced retrieval/reranking → hybrid search → evaluation → production deployment → capstone. Each module includes practical exercises with real tools, and the program closes with hands-on RAG projects and an enterprise RAG capstone.

Module 1: RAG & Enterprise Knowledge AI Foundations — 8 Hours

Introduction to RAG and enterprise knowledge AI
How RAG differs from fine-tuning and prompt-only approaches
Enterprise knowledge sources: documents, databases, APIs
RAG architecture overview: ingestion, retrieval, generation
Use cases: knowledge assistants, enterprise search, AI copilots
Challenges: accuracy, freshness, privacy and scale

Module 2: Python for RAG Engineering — 10 Hours

Python syntax, data structures and control flow
Functions, modules and packages
Working with APIs, JSON and environment variables
Virtual environments and dependency management
File I/O and handling document formats
Error handling and debugging for RAG applications

Module 3: LLM Fundamentals & API Integration — 10 Hours

LLM basics: tokens, context windows, temperature
Overview of LLM APIs (OpenAI/Gemini/Claude-style)
Authentication, rate limits and error handling
Streaming responses and async calls
Prompt basics for RAG-grounded answers
Building reusable API wrappers in Python

Module 4: Document Processing & Data Ingestion — 8 Hours

Document formats: PDF, HTML, DOCX, Markdown, CSV
Text extraction and cleaning pipelines
Metadata extraction and enrichment
Handling tables, images and structured content
Incremental ingestion and update strategies
Hands-on: building a document ingestion pipeline

Module 5: Text Chunking & Document Transformation — 8 Hours

Chunking strategies: fixed-size, recursive, semantic
Overlap, chunk size and their impact on retrieval
Document transformation and normalization
Metadata tagging for filtered retrieval
Handling multi-document and hierarchical content
Hands-on: comparing chunking strategies

Module 6: Embeddings & Semantic Search — 10 Hours

Text embeddings and semantic similarity
Embedding models: OpenAI, Hugging Face, open-source
Similarity metrics: cosine, dot product, Euclidean
Building and querying a semantic search index
Evaluating embedding quality for retrieval
Hands-on: building a semantic search pipeline

Module 7: Vector Databases & Indexing — 10 Hours

Vector databases: FAISS, ChromaDB, Pinecone, pgvector
Index types: flat, IVF, HNSW and trade-offs
Upserting, updating and deleting vectors
Metadata filtering and namespaces
Performance, scaling and cost considerations
Hands-on: building a vector search index

Module 8: Retrieval-Augmented Generation (RAG) — 12 Hours

RAG architecture: retriever, context builder, generator
Building end-to-end RAG pipelines with LangChain
Building RAG pipelines with LlamaIndex
Prompt design for grounded answers
Handling citations and source attribution
Hands-on: building a production-style RAG application

Module 9: Advanced Retrieval & Reranking — 10 Hours

Multi-query and query rewriting strategies
Reranking with cross-encoders and LLMs
Context compression and window expansion
Handling multi-hop and comparative queries
Retrieval fallback and error handling
Hands-on: adding reranking to a RAG pipeline

Module 10: Hybrid Search & Knowledge Retrieval — 10 Hours

Combining keyword (BM25) and vector search
Reciprocal rank fusion and score normalization
Elasticsearch/OpenSearch for hybrid retrieval
Metadata filters and structured queries
Knowledge graph integration basics
Hands-on: building a hybrid search system

Module 11: RAG Evaluation, Accuracy & Optimization — 12 Hours

Retrieval metrics: recall, precision, MRR, nDCG
Generation metrics: faithfulness, answer relevance
Building evaluation datasets for RAG
Automated evaluation with LLM-as-judge
Diagnosing and fixing retrieval failures
Hands-on: evaluating a RAG pipeline end-to-end

Module 12: Production RAG, APIs & Deployment — 12 Hours

Exposing RAG pipelines as APIs with FastAPI
Latency, caching and throughput optimization
Cost management for embeddings and LLM calls
Containerization and deployment with Docker
Monitoring, logging and observability for RAG
Hands-on: deploying a RAG API to the cloud

Module 13: Enterprise RAG Projects & Capstone — 20 Hours

End-to-end RAG application design and architecture
Ingestion + embeddings + vector DB + retrieval integration
Evaluation and optimization of the capstone application
Code review and portfolio preparation
Presentation and documentation of capstone project
Interview preparation around capstone work

#	Module	Hours	Tools
1	RAG & Enterprise Knowledge AI Foundations	8	Python, LLM APIs
2	Python for RAG Engineering	10	Python, Git
3	LLM Fundamentals & API Integration	10	OpenAI APIs, Hugging Face
4	Document Processing & Data Ingestion	8	Python, LlamaIndex
5	Text Chunking & Document Transformation	8	LangChain, LlamaIndex
6	Embeddings & Semantic Search	10	Hugging Face, OpenAI Embeddings
7	Vector Databases & Indexing	10	FAISS, ChromaDB, Pinecone, pgvector
8	Retrieval-Augmented Generation (RAG)	12	LangChain, LlamaIndex, Vector DBs
9	Advanced Retrieval & Reranking	10	LangChain, Cross-Encoders
10	Hybrid Search & Knowledge Retrieval	10	Elasticsearch/OpenSearch, LangChain
11	RAG Evaluation, Accuracy & Optimization	12	RAGAS, LangSmith, Python
12	Production RAG, APIs & Deployment	12	FastAPI, Docker, Cloud Deployment Tools
13	Enterprise RAG Projects & Capstone	20	All tools
Total		140 Hours	15+ tools

Contacted by

Mr. Irshad

Curriculum & Technical Reviewer

[8+ Years Of Experience]

Specialization: RAG Engineering, Generative AI, Retrieval Systems

LinkedIn Profile

Get Your RAG Engineering Certification In Rachi

On completing all 13 modules, the assignments and the Enterprise RAG capstone project, you receive the Uncodemy RAG Engineering Program Certificate. The certificate records the program name, duration, tools covered and completion date. It is verifiable online at certificate.uncodemy.com and provides a record of successful completion of the Uncodemy RAG Engineering program.

The certificate is tied to the practical work completed during the program — including hands-on RAG pipeline work, retrieval evaluation and the 20-hour Enterprise RAG capstone project. Learners work with Python, OpenAI APIs, Hugging Face, LangChain, LlamaIndex, LangGraph, FAISS, ChromaDB, Pinecone, PostgreSQL/pgvector, Elasticsearch/OpenSearch, FastAPI, Docker, Git/GitHub and cloud deployment tools.

Key Benefits of Our RAG Engineering Certification:

Program Completion :Provides a record of successful completion of the RAG Engineering program.
Online Verification :Certificate is verifiable online at certificate.uncodemy.com.
Practical Learning :Training includes hands-on RAG pipelines, retrieval evaluation and an enterprise capstone.

This is an independent Uncodemy certificate. It is not affiliated with, endorsed by, or a credential of OpenAI, Pinecone or any other RAG/vendor provider whose tools are taught in this course. Certificate verification process pending confirmation.

Uncodemy Certificate Verification Online Certificate Verification & Download certificate.uncodemy.com

IIBA Endorsed Education Provider certificate — Uncodemy

Uncodemy RAG Engineering Program Certificate

Tools and Technologies Covered

OpenAI APIs
LangChain
LlamaIndex
Vector Databases
Hugging Face
FAISS
ChromaDB
Pinecone
FastAPI
Docker
<Elasticsearch
Git & GitHub

Apply Now

Introducing Uncodemy

Uncodemy is a career-oriented IT training institute managed by Uncodemy Edutech Pvt. Ltd. Backed by 14+ years of training experience, the institute operates classroom centres in Delhi and Noida while also providing live online training to learners throughout India. Uncodemy offers instructor-led programs focused on modern and emerging technologies.

In this RAG Engineering program, learners build retrieval pipelines, hybrid search systems, production RAG APIs and complete an enterprise RAG capstone project. The training covers how retrieval-grounded AI systems are engineered in production — document processing, embeddings, vector search, retrieval evaluation and deployment. You can read the full module-by-module curriculum before you enrol.

Alongside training, Uncodemy provides instructor-led sessions, practical assignments, project-based learning, interview preparation and career support. What that support includes — and what it does not — is set out in our career support policy, along with the eligibility conditions published before enrolment rather than after payment.

Other courses for Rachi learners

If you are still deciding on a direction, Uncodemy also runs LLM Engineering, AI Testing and Data Science courses for Rachi and Jharkhand learners.

Portfolio/capstone project review:Your hands-on RAG pipelines, retrieval evaluation work and enterprise RAG capstone are reviewed and refined into a portfolio an interview will find credible.

Resume & LinkedIn:We build your resume around RAG engineering keywords and measurable outcomes, and align your LinkedIn profile to match.

Interview preparation:Retrieval pipeline design questions, chunking-strategy trade-offs, evaluation-metric discussions and RAG engineering case studies specific to AI hiring.

Mock interviews:Sessions with industry trainers, followed by written feedback on what to fix.

Opportunity sharing:Relevant openings from our hiring network of 850+ companies are shared with you, with support through the application and interview process.

Continued support:This is active assistance, not a guarantee of employment — no training institute can honestly promise a job. Support continues after your program ends for learners who stay engaged with the process.

Review

4.7/5500+ Google Reviews

5/530+ Sulekha Reviews

4.7/5500+ Just Dial Reviews

5/5Facebook Reviews

4/5130+ Mouthshut

5/554+ Trustindex Reviews

Instructors

The people who actually teach this course

Uncodemy separates two roles that most institutes combine. Trainers deliver the live sessions. Domain reviewers — separate specialists who don't teach the batch — verify that the curriculum is technically accurate and current. For this program, curriculum and technical content are reviewed by Mr. Irshad, with additional review by Mr. Upendra Kumar Tiwari. It means the person teaching a course is not the only person checking whether its content is correct.

Every trainer and domain reviewer associated with Uncodemy is published by name, with their domain and background — you can look them up before you enrol rather than after.

Enroll in Our RAG Engineering Training for Rachi Learners Today!

Start your RAG Engineering journey with Uncodemy’s course for learners in Pune. Build practical expertise in document processing, embeddings, vector databases, retrieval pipelines, and hybrid search through live instructor-led sessions, hands-on projects, and an enterprise RAG capstone. Gain structured technical training and career preparation to strengthen your foundation in RAG engineering.

RAG Engineer Jobs and Career Scope in Rachi+

Rachi's tech landscape — covering software services, captive units, fintech, SaaS and automotive R&D — is progressively creating in-house knowledge assistants, enterprise search solutions and AI copilots. Firms in Hinjewadi, Magarpatta, Kharadi, Baner and Viman Nagar are embedding RAG-driven capabilities into support platforms, document pipelines and internal tools, generating need for engineers who grasp document ingestion, chunking, embeddings, vector databases, retrieval workflows and hybrid search. BFSI, manufacturing-tech, healthcare, edtech and e-commerce verticals in Rachi are likewise embracing RAG, and they require engineers who can anchor LLM responses in actual enterprise data instead of depending on model training alone. Interviews for RAG positions tend to emphasise retrieval pipeline architecture, chunking-strategy trade-offs and evaluation methodology most strongly. Applicants with software engineering or data/ML backgrounds alongside RAG engineering abilities are ideally placed for these positions.

Job Roles After This Course+

RAG Engineer, AI Application Engineer, GenAI Engineer, LLM/RAG Engineer, AI Solutions Engineer (Junior) and Search/Retrieval Engineer are the positions this course corresponds to. Every position entails creating retrieval-grounded AI systems, yet they vary in the particular RAG components and tools employed.

Role	Core Tools
RAG Engineer	LangChain, LlamaIndex, Vector DBs, Python
AI Application Engineer	LangChain, FastAPI, Vector DBs
GenAI Engineer	OpenAI APIs, LangChain, Embeddings
LLM/RAG Engineer	LangChain, LlamaIndex, Evaluation Tools
AI Solutions Engineer (Junior)	FastAPI, Docker, Cloud Deployment Tools
Search/Retrieval Engineer	Elasticsearch/OpenSearch, Vector DBs, Python

Most graduates begin as a RAG Engineer, AI Application Engineer or GenAI Engineer and advance into the others with two to four years of experience, as they develop depth in a particular RAG component or domain. Advancement generally entails owning bigger retrieval systems, leading projects, and specialising in evaluation, hybrid search or production deployment.

RAG Engineer Salary in Rachi — Role-wise+

Salaries in Rachi differ by experience level, sector and how robust your RAG engineering abilities are. The gap between the lowest and highest earners at the same title is frequently wider than freshers anticipate.

Role	Salary Range
Entry-Level RAG Engineer	₹5–9 LPA
RAG / GenAI Engineer	₹8–15 LPA
AI Application Engineer	₹8–16 LPA
LLM / RAG Engineer	₹10–20 LPA
Senior RAG Engineer	₹18–30 LPA
AI Solutions Engineer	₹15–28 LPA
Lead AI / RAG Engineer	₹25–40 LPA

Sources reviewed: AmbitionBox, Glassdoor, Indeed and Uncodemy placement records. Reviewed: September 2026.

Salary ranges are indicative market ranges and should not be presented as guaranteed placement outcomes. Actual offers vary by role, skills, experience, employer and market conditions. Uncodemy does not guarantee any specific salary or employment outcome.

Top Sectors Hiring RAG Engineers in Rachi+

Uncodemy collaborates with 850+ companies in our hiring network across India, covering large enterprises, mid-size firms and fast-growing startups, which provides learners exposure to more than one kind of hiring process and interview style. In Rachi specifically, RAG engineering demand is rising in:

IT services and global capability centres — Hinjewadi, Kharadi and Magarpatta, creating internal knowledge assistants and enterprise search
Product companies and SaaS startups — creating AI copilots, RAG-based features and document Q&A systems
BFSI and fintech — implementing RAG for policy retrieval, compliance and customer support
Automotive and manufacturing-tech — RAG for technical documentation, diagnostics and knowledge bases
Healthcare, edtech and e-commerce — RAG for content retrieval, personalization and customer service

Every one of these sectors tends to weight skills somewhat differently, so as you get nearer to placement, it helps to tailor your capstone project toward the one or two sectors you're most interested in.

How to Become a RAG Engineer — Step by Step+

Becoming a RAG engineer is a step-by-step skill build, not a single course, and skipping steps usually shows up later as gaps in interviews or on the job.

Python fundamentals— syntax, data structures, APIs and environment management.
LLM fundamentals & API integration— tokens, context windows, API authentication and streaming.
Document processing & chunking— extraction, cleaning, chunking strategies and metadata.
Embeddings & semantic search— embedding models, similarity metrics and index building.
Vector databases— FAISS, ChromaDB, Pinecone, pgvector, indexing and filtering.
RAG pipeline & advanced retrieval— LangChain/LlamaIndex, reranking, hybrid search.
Evaluation, production deployment & capstone— RAGAS, LangSmith, FastAPI, Docker and the enterprise RAG project.

Typical timeline: 5–6 months of consistent study to reach interview-ready standard, assuming focused hours most days rather than occasional binge sessions.

How This Course Compares to Other AI Courses in Rachi+

Rachi has dozens of AI training options at very different price points, ranging from short weekend workshops to multi-month bootcamps. Here is an honest comparison, so you can judge the fit yourself rather than relying on marketing claims alone.

Factor	This Program
Duration	5–6 months
Learning hours	140+
Live sessions	90+
Tools	15+
Capstone	20-hour Enterprise RAG project
Career support	Dedicated Career & Placement Assistance

Compared to Uncodemy's LLM Engineering course (5–6 months, focused on building, fine-tuning and deploying LLM-powered applications broadly) and Generative AI & Agentic AI course (~3–4 months, focused on GenAI apps and autonomous agents), this RAG Engineering program is the most specialised in retrieval-grounded systems — it includes dedicated modules on document processing, chunking, embeddings, vector databases, advanced retrieval, hybrid search and evaluation. If your goal is a full RAG engineering career transition, this is the right fit. If you want broad LLM application development, LLM Engineering is a closer match. If you are specifically building AI agents, the Generative AI & Agentic AI course is a closer match.

Where we are not the cheapest option: shorter self-paced courses cost less. If your goal is a quick RAG refresher rather than a full career transition, a shorter program may suit you better, and it's worth being honest with yourself about which of the two you actually need.

Online vs Offline vs Hybrid — Which Should You Choose?+

Offline classroom is available at our Delhi and Noida centres; Rachi learners typically join live online. Live online gives you the same instructor-led experience with zero commute, and all sessions are recorded for revision. Offline suits learners who prefer face-to-face interaction and are based near Delhi or Noida. Hybrid is not currently offered for this program. For Rachi-based working professionals, live online is usually the most practical choice — you get real-time doubt clearing, hands-on labs and lifetime access to recordings without travelling.

Is this course right for you?+

If you want...	Recommend
Full RAG Engineering career transition	This program
Already know LLM Engineering fundamentals, want to specialise in retrieval systems	This program
Building AI agents specifically	Consider Generative AI & Agentic AI course
Broad LLM application development	Consider LLM Engineering course

Frequently Asked Questions

1. What is the duration of the RAG Engineering course in Rachi?▼

The program runs for 5–6 months and includes 140+ hours of learning across 90+ live sessions. Weekday and weekend batches are both available.

2. Do I need prior Python or LLM experience?▼

Programming fundamentals are helpful but not mandatory — Python is taught from Module 2 onward. Familiarity with basic LLM concepts is a plus but built up in Module 3. A laptop with stable internet is required.

3. What tools are covered in this course?▼

15+ tools including Python, OpenAI APIs, Hugging Face, LangChain, LlamaIndex, LangGraph, FAISS, ChromaDB, Pinecone, PostgreSQL/pgvector, Elasticsearch/OpenSearch, FastAPI, Docker, Git/GitHub and cloud deployment tools.

4. Does the course cover vector databases and hybrid search?▼

Yes. Module 7 covers Vector Databases & Indexing (10 hours) and Module 10 covers Hybrid Search & Knowledge Retrieval (10 hours). Both are core components with hands-on exercises.

5. What is the capstone project?▼

The capstone is Module 13: Enterprise RAG Projects & Capstone (20 hours). You build an end-to-end RAG application applying document processing, embeddings, vector search, retrieval, evaluation and deployment.

6. Are the classes live or pre-recorded?▼

All 90+ sessions are live and instructor-led, with recordings available afterwards for revision.

7. What is the fee for the RAG Engineering course in Rachi?▼

The program fee is ₹17,500 plus 18% GST. Total fee is ₹20,650. The same fee applies in all modes and cities.

8. Are EMI options available?▼

EMI starts from ₹4,032 per month. The exact amount, tenure and eligibility depend on the applicable payment or financing option — a counsellor can confirm your plan.

9. Is there a demo or trial class before I enrol?▼

Yes. You can attend a demo session to see the teaching style, meet the trainer and review the curriculum before deciding. Contact us to book a demo.

10. Do you provide placement assistance?▼

We provide Dedicated Career & Placement Assistance — resume building, LinkedIn optimisation, portfolio review, interview preparation, mock interviews and opportunity sharing through our network of 850+ companies. We do not guarantee employment.

11. Can I get an entry-level RAG Engineer role after this course?▼

Entry-level RAG Engineer roles in the market range around ₹5–9 LPA (indicative). Outcomes vary by skills, experience, employer and market conditions. Uncodemy does not guarantee any specific salary or employment outcome.

12. What is the salary of a RAG Engineer in Rachi?▼

Indicative market ranges: Entry-Level ₹5–9 LPA, RAG/GenAI Engineer ₹8–15 LPA, Senior RAG Engineer ₹18–30 LPA, Lead AI/RAG Engineer ₹25–40 LPA. Actual offers vary by role, skills, experience, employer and market conditions. Uncodemy does not guarantee any specific salary.

13. What job roles can I apply for after this course?▼

RAG Engineer, AI Application Engineer, GenAI Engineer, LLM/RAG Engineer, AI Solutions Engineer (Junior) and Search/Retrieval Engineer.

14. Who is eligible for this RAG Engineering course?▼

Developers wanting to specialise in retrieval-grounded AI systems, data scientists/ML engineers upskilling into RAG engineering, aspiring AI engineers, and working professionals moving into GenAI/RAG roles.

15. Are the salary, placement and review figures on this page guaranteed?▼

Statistics are based on Uncodemy's internal records and publicly available review-platform information. Placement and salary outcomes vary by learner, skills, experience, location, employer and market conditions. Career and placement assistance does not constitute a guarantee of employment or a specific salary package.

16. Is classroom training available in Rachi?▼

This course is delivered live online nationwide, including for Rachi learners. Classroom training is available at our Delhi and Noida centres for other courses. Rachi learners typically join live online.

17. Is Uncodemy's RAG Engineering certificate affiliated with OpenAI or Pinecone?▼

No. This is an independent Uncodemy certificate. It is not affiliated with, endorsed by, or a credential of OpenAI, Pinecone or any other RAG/vendor provider whose tools are taught in this course.

Why Trust This Page? Discover Why Students Choose Uncodemy

How we maintain this page

Content written by:Mr. Rahul
Content rechecked and verified by: Mr. Irshad
Course curriculum verified by:Mr. Irshad
Technical content verified by: Mr. Irshad
Fee verified by: Admin Department
Placement figures sourced from internal placement records
Review counts verified against respective third-party platforms
Last reviewed: 25 Sep 2026

Written by:Mr. Rahul

Reviewed by: Mr. Upendra Kumar Tiwari

Final editorial check:Mr. Gangesh Gaunr, Director at Uncodemy

Application Form

Name

Email Address

Phone Number

Location

Course

Visit Us

Nearest Classroom Training Centres

The RAG Engineering course is delivered fully live online. Rachi learners typically join live online, and our nearest classroom training centres for other courses are in Delhi and Noida. Uncodemy's Delhi centre is located on the 2nd & 3rd Floor, Maa Katyayni Complex, MB-1E, Madhuban Road, Shakarpur Extension — a 10–15 minute walk from Nirman Vihar Metro Station and Laxmi Nagar Metro Station. Uncodemy's Noida centre is at B 14-15, Udhyog Marg, Block B, Sector 1, Noida.

Open now

Delhi Centre

2nd & 3rd Floor, Maa Katyayni Complex, MB-1E, Madhuban Rd, next to DSEU Ambedkar College, Laxmi Nagar, Shakarpur Extension, Shakarpur, Delhi, 110092

Noida Centre

B, 14-15, Udhyog Marg, Block B, Sector 1, Noida, Uttar Pradesh 201301

Phone+91 9818366550 / +91 8766313646

Emailinfo@uncodemy.com

Free demo classWalk-ins welcomeWheelchair accessible

Delhi Map Noida Map

⇧

RAG Engineering Training Course in Rachi by Uncodemy

Special Offer: RAG Engineering Program

Expert Trainers

Hands-on Automation Projects

Lifetime Access

Dedicated Career & Placement Assistance

Certified Course

Flexible Batches

Why ChooseUncodemy?

Program Details

RAG Engineering Course in Rachi — Quick Facts

List of Placed Students Trained by Uncodemy

What is RAG Engineering?

RAG Engineering vs LLM Engineering vs Generative AI

Course Overview

Weekdays

Weekends

Curriculum for RAG Engineering Training Course in Rachi

RAG Engineering Course Syllabus in Rachi

Module 1: RAG & Enterprise Knowledge AI Foundations — 8 Hours

Module 2: Python for RAG Engineering — 10 Hours

Module 3: LLM Fundamentals & API Integration — 10 Hours

Module 4: Document Processing & Data Ingestion — 8 Hours

Module 5: Text Chunking & Document Transformation — 8 Hours

Module 6: Embeddings & Semantic Search — 10 Hours

Module 7: Vector Databases & Indexing — 10 Hours

Module 8: Retrieval-Augmented Generation (RAG) — 12 Hours

Module 9: Advanced Retrieval & Reranking — 10 Hours

Module 10: Hybrid Search & Knowledge Retrieval — 10 Hours

Module 11: RAG Evaluation, Accuracy & Optimization — 12 Hours

Module 12: Production RAG, APIs & Deployment — 12 Hours

Module 13: Enterprise RAG Projects & Capstone — 20 Hours

Contacted by

Mr. Irshad

Get Your RAG Engineering Certification In Rachi

Key Benefits of Our RAG Engineering Certification:

Tools and Technologies Covered

Introducing Uncodemy

Other courses for Rachi learners

Instructors

Enroll in Our RAG Engineering Training for Rachi Learners Today!

RAG Engineer Jobs and Career Scope in Rachi+

Job Roles After This Course+

RAG Engineer Salary in Rachi — Role-wise+

Top Sectors Hiring RAG Engineers in Rachi+

How to Become a RAG Engineer — Step by Step+

How This Course Compares to Other AI Courses in Rachi+

Online vs Offline vs Hybrid — Which Should You Choose?+

Is this course right for you?+

Frequently Asked Questions

1. What is the duration of the RAG Engineering course in Rachi?▼

2. Do I need prior Python or LLM experience?▼

3. What tools are covered in this course?▼

4. Does the course cover vector databases and hybrid search?▼

5. What is the capstone project?▼

6. Are the classes live or pre-recorded?▼

7. What is the fee for the RAG Engineering course in Rachi?▼

8. Are EMI options available?▼

9. Is there a demo or trial class before I enrol?▼

10. Do you provide placement assistance?▼

11. Can I get an entry-level RAG Engineer role after this course?▼

12. What is the salary of a RAG Engineer in Rachi?▼

13. What job roles can I apply for after this course?▼

14. Who is eligible for this RAG Engineering course?▼

15. Are the salary, placement and review figures on this page guaranteed?▼

16. Is classroom training available in Rachi?▼

17. Is Uncodemy's RAG Engineering certificate affiliated with OpenAI or Pinecone?▼

Why Trust This Page? Discover Why Students Choose Uncodemy

How we maintain this page

Nearest Classroom Training Centres

Why Choose
Uncodemy?