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Techcadd
RAG Development Course in Mohali

Build Production-Grade
Retrieval-Augmented Generation Systems

Go beyond prompt engineering. Learn to design real RAG pipelines — chunking, embeddings, vector search, re-ranking, and grounded generation — so your AI applications answer from real, private, and up-to-date data instead of guessing.

View Curriculum
Hands-on with the tools RAG teams use in production
LangChain LlamaIndex Pinecone ChromaDB OpenAI API Hugging Face
User Query"What's our refund policy for enterprise plans?"
Embed & RetrieveVector search across private knowledge base
Re-rank & GroundTop-k passages fused into context window
Grounded AnswerCited response generated by the LLM
RAG Engineering Capabilities

Everything You Need to Engineer Retrieval-Augmented Systems

Data Ingestion & Chunking Strategies

Learn to parse PDFs, docs, HTML, and APIs into clean text, then split it using fixed-size, semantic, and recursive chunking strategies that preserve meaning instead of breaking it mid-sentence.

Higher retrieval precision Fewer broken contexts Format-aware parsing
"Chunking is where most RAG systems quietly fail — we teach it as a first-class skill, not an afterthought."
Raw DocumentsPDF, DOCX, HTML, Markdown, APIs
Semantic ChunkingRecursive splitters with overlap control
Metadata TaggingSource, section & timestamp enrichment

Embeddings & Vector Search Fundamentals

Convert text into dense vector representations, understand distance metrics, and index them in production vector databases for millisecond semantic search at scale.

Cosine vs. dot-product search Index tuning (HNSW, IVF) Embedding model selection
"Understanding how embeddings actually cluster meaning is what separates a working retriever from a guessing one."
Text ChunksPassed to an embedding model
Dense Vectors1536-dim embeddings generated
Vector IndexStored in Pinecone / ChromaDB / FAISS

Retrieval Orchestration with LangChain & LlamaIndex

Build retrieval chains that combine hybrid search (keyword + vector), metadata filters, and re-ranking models to surface the most relevant context for every query.

Hybrid search pipelines Cross-encoder re-ranking Multi-hop retrieval
"Retrieval is the engine of RAG — get this stage right and generation quality follows naturally."
QueryRewritten & expanded for recall
Hybrid SearchVector + keyword (BM25) fusion
Re-ranked Top-KCross-encoder scoring applied

Prompting & Grounded Generation

Design context-fusion prompts that keep the LLM grounded in retrieved evidence, cite sources, and gracefully say "I don't know" instead of hallucinating an answer.

Context window management Source citation formatting Hallucination reduction
"A grounded prompt template is the difference between an assistant users trust and one they double-check."
Context FusionRetrieved passages + system prompt
LLM GenerationGPT-4o, Claude, Llama 3, Gemini
Cited AnswerResponse with source references

Evaluation & Guardrails

Measure retrieval and answer quality with faithfulness, relevance, and precision metrics, then set guardrails to catch hallucinations and unsafe outputs before they reach users.

RAGAS-style evaluation Faithfulness scoring Automated regression tests
"If you can't measure a RAG pipeline, you can't trust it in production — evaluation is built in from day one."
Faithfulness CheckAnswer vs. retrieved evidence
GuardrailsToxicity, PII & scope filters
Quality DashboardTracked across every release

Deployment, Scaling & Monitoring

Package RAG pipelines behind fast APIs, cache embeddings and results, and monitor latency, cost, and answer quality once real users start querying the system.

Low-latency API design Cost & token monitoring Streaming responses
"A RAG demo is easy. A RAG system that stays fast and accurate at scale is the actual skill employers pay for."
FastAPI ServiceContainerized retrieval + generation
Caching LayerEmbeddings & frequent queries
Live MonitoringLatency, cost & drift alerts
Why Learners Choose This Program

Hands-On RAG Engineering. Real Vector Pipelines. Career-Ready Skills.

Reason 01

Project-Based Curriculum

Every module ends with a working pipeline you build, break, and fix — not slides.

Reason 02

Beginner to Production Path

Start from LLM basics and progress to deployed, monitored RAG systems.

Reason 03

Hands-On Vector DB Labs

Work directly inside Pinecone, ChromaDB, FAISS, and Weaviate on real datasets, not toy examples.

Reason 04

Mentor-Led Code Reviews

Industry mentors review your retrieval logic and prompts, catching mistakes early.

Reason 05

Placement & Portfolio Support

Leave with a deployed RAG project, GitHub portfolio, and 100% placement assistance.

Course Details

RAG Development Course Details at a Glance

Duration

3 to 5 months, depending on batch pace

Mode of Learning

Classroom training in Mohali & Chandigarh, plus live online batches

Eligibility

Developers & data professionals — basic Python helpful, not required

Batch Options

Weekday, weekend and evening batches available

Certification

Industry-recognized Techcadd RAG Development Certificate

Fees

Affordable fee structure with EMI options — ask your counsellor

The Pipeline Behind Production RAG

Modern Retrieval & Generation Technologies, Engineered for Scale

We combine vector databases, orchestration frameworks, and top-tier LLMs to teach retrieval-augmented systems the way they're built in production.

Loaders
Vector Store
Chunking
Embeddings
Metadata Filters
Retrieval Layer
Generation Layer
Prompt Templates
Re-ranking
Context Fusion
Guardrails
Streaming Output
Course Curriculum

A Structured Path From LLM Fundamentals to Production RAG

01

LLM & Prompting Foundations

Tokens, context windows, prompt design, and how large language models actually generate text.

02

Text Processing, Chunking & Embeddings

Cleaning raw documents, chunking strategies, and generating high-quality vector embeddings.

03

Vector Databases

Indexing and querying Pinecone, ChromaDB, FAISS, and Weaviate at scale.

04

Retrieval Orchestration

Building retrieval chains with LangChain and LlamaIndex, including hybrid search.

05

Advanced Retrieval Techniques

Re-ranking, multi-hop reasoning, query rewriting, and metadata filtering.

06

Evaluation & Hallucination Control

Faithfulness metrics, RAG evaluation frameworks, and guardrail design.

07

Agentic RAG

Combining retrieval with tool-use and multi-step agent reasoning for complex queries.

08

Deployment & Capstone Project

Shipping a production-ready RAG API with monitoring, caching, and a portfolio-ready capstone.

How We Teach

A Learning Process Built for Real-World RAG Delivery

1

Foundations & Environment Setup

Python refresher, LLM APIs, and setting up the tooling you'll use throughout the program.

2

Data Ingestion & Preprocessing Labs

Parse real documents and design chunking strategies suited to different content types.

3

Embedding & Vector Store Implementation

Generate embeddings and build searchable vector indexes across multiple databases.

4

Retrieval Pipeline Engineering

Combine hybrid search, filters, and re-ranking into a single retrieval chain.

5

Generation, Evaluation & Guardrails

Ground LLM responses, measure quality, and add safety guardrails before shipping.

6

Capstone Project, Deployment & Portfolio Review

Deploy a full RAG application and walk away with a reviewed, portfolio-ready project.

Ready to Build RAG Systems That Actually Work?

Join the RAG Development Course in Mohali and go from LLM basics to deployed, evaluated retrieval-augmented applications under expert guidance.

Got Questions?

Frequently Asked Questions

What exactly is covered in this RAG Development course?

You will learn the full RAG pipeline: document ingestion and chunking, embeddings and vector databases, retrieval orchestration with LangChain/LlamaIndex, advanced retrieval (re-ranking, multi-hop), evaluation and guardrails, and production deployment.

Do I need prior experience with LLMs or vector databases?

Basic Python is helpful but not required. The first module covers LLM and prompting foundations before any retrieval or vector database concepts are introduced.

How long does the course take and what batch options are available?

The program typically runs 3 to 5 months, with weekday, weekend and evening batches so working professionals and students can both attend comfortably.

How is this different from the Generative AI or Prompt Engineering courses at Techcadd?

This course goes deep on one specific skill: grounding LLMs in real, private data through retrieval. Prompt Engineering focuses on directing a model well, and Generative AI covers the broader GenAI application landscape — RAG Development is for engineers who want to specialize in retrieval and vector search.

Which vector databases and frameworks will I actually use?

You will get hands-on lab time with Pinecone, ChromaDB, FAISS, and Weaviate for vector storage, and LangChain and LlamaIndex for retrieval orchestration, alongside OpenAI, Claude, and Hugging Face models for generation.

Will I build a real RAG application, or is this mostly theory?

The program is lab-driven. Every module ends with a working pipeline you build and debug, culminating in a deployed, monitored capstone RAG API you'll present as your portfolio project.

Is job placement assistance included?

Yes. Techcadd provides 100% placement assistance, including resume building, mock interviews, and direct connections with hiring partners across Mohali and Chandigarh.

What certificate do I receive after completing the course?

You receive an industry-recognized Techcadd RAG Development Certificate, validating your practical ability to design, evaluate and deploy retrieval-augmented generation systems.

What job roles can I target after this course?

Graduates typically move into roles such as RAG Engineer, AI/LLM Application Developer, AI Engineer, or Machine Learning Engineer focused on retrieval and search.

Will I learn how to reduce hallucination and evaluate answer quality?

Yes. The evaluation and guardrails module covers faithfulness and relevance scoring, RAGAS-style evaluation frameworks, and guardrail design — this isn't an afterthought, it's a full module before deployment.

Is there support for beginners with limited coding background?

Yes. The foundations module covers the Python and LLM API basics you need before retrieval-specific content begins, and mentors are available throughout for one-on-one doubt-clearing.

How do I get pricing and the detailed syllabus?

Fees, the detailed module-wise syllabus and current batch schedules are shared during a free counselling session — book a free demo class to get the full breakdown.

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