Learn Ai
Retrieval-augmented-generation
Learn how to use Techpratham to master Retrieval-Augmented Generation (RAG) to make smart AI systems that can get information from outside sources and generate language. Find out how RAG improves LLMs by giving them real-time, accurate, and domain-specific answers.
5/5(4,890 Reviews)
Level
Advanced
Duration
8 weeks
What is Retrieval-augmented-generation ?
Techpratham's Retrieval-Augmented Generation course is meant to teach students how to use advanced AI methods to create text that is based on knowledge. Retrieval pipelines, vector databases, embeddings, and LLM integration are all covered in the training. You'll learn how to use tools like Pinecone, FAISS, and LangChain to build RAG-based solutions that can grow. By the end, students will know how to make RAG systems for chatbots, search engines, and decision-support tools that work for big businesses.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks
The candidate will get access to the presentation taken-up during the session.
Notes
Consolidated notes in word document.
Assignments
Assignments for every module will be covered.
MCQs
MCQs for every module covered in the session would be provided.
Video Recording
Daily Session would be recorded and shared to the candidate.
Dumps for Certification
We will provide dumps for the certification exam, which will help you to prepare for the exam.
Updated Content
We provide Generative AI Driven content By experts.
Projects
3 Live Projects will be provided for practice.
Retrieval-augmented-generation Course Curriculum
.
Introduction to Retrieval-Augmented Generation (RAG)
Section 1
Understand the fundamentals and importance of RAG in AI.
RAG Architecture Overview
Role in Enhancing LLMs
Retrieval vs Generation
Industry Use Cases
Benefits & Limitations
.
Information Retrieval Basics
Section 2
Learn core retrieval techniques used in RAG, including vector search, embeddings, and similarity measures.
Embeddings
Vector Databases
Similarity Search
Indexing
.
Knowledge Sources for RAG
Section 3
Explore different external data sources and methods to structure knowledge for efficient retrieval.
Structured vs Unstructured Data
Knowledge Bases
Document Stores
APIs & External Sources
.
Building the Retrieval Pipeline
Section 4
Develop a retrieval pipeline with embedding models, chunking strategies, and indexing for scalable retrieval.
Text Chunking
Embedding Models
Index Creation
Query Processing
.
Generation Layer in RAG
Section 5
Understand how LLMs integrate retrieved context to generate accurate and coherent responses.
Context Injection
Prompt Engineering
Fusion Strategies
Coherence & Accuracy
.
RAG Architectures & Frameworks
Section 6
Study standard RAG architectures and frameworks like LangChain, LlamaIndex, and Hugging Face implementations.
LangChain RAG
LlamaIndex
Hugging Face RAG
Hybrid Architectures
.
Fine-tuning & Optimization
Section 7
Optimize retrieval and generation performance through fine-tuning models and adjusting hyperparameters.
Model Fine-tuning
Retrieval Optimization
Re-ranking Techniques
Latency vs Accuracy
.
Evaluation of RAG Systems
Section 8
Learn to evaluate RAG outputs using both quantitative metrics and human-in-the-loop evaluations.
Precision & Recall
BLEU/ROUGE Scores
Hallucination Detection
Human Feedback
.
Scalability & Deployment
Section 9
Deploy RAG systems efficiently for enterprise-scale use cases with focus on scalability and monitoring.
Cloud Deployment
Vector Store Scaling
Monitoring Pipelines
API Endpoints
.
Security, Compliance & Responsible AI in RAG
Section 10
Ensure ethical, secure, and compliant use of RAG systems in sensitive domains.
Data Privacy
Access Controls
Bias Mitigation
Compliance Standards
Key Projects
Retrieval-augmented-generation
Intelligent Legal Document Assistant
Use FAISS to make a legal chatbot that gets case laws and rules from a knowledge base. The assistant gives answers based on facts, making sure that lawyers can quickly find the right legal references.
Healthcare Knowledge Retrieval Chatbot
Use LangChain and Pinecone to make a healthcare-focused RAG chatbot. It will get the right medical documents, research papers, and guidelines to give doctors information based on evidence.
Enterprise Knowledge Base Search Engine
Build a smart search engine for the whole company that uses RAG. The system organizes internal documents and works with LLMs to give employees answers that are relevant, correct, and specific to their field.
Retrieval-augmented-generation – Associate Training Program
Category
Associate
Exam Name:
Retrieval-augmented-generation – Associate
Exam Code:
N.A.
Exam Duration:
N.A.
Exam Format:
N.A.
Passing Score:
N.A.
Our Testimonials
Frequently Asked Questions (FAQs)
How is Retrieval-Augmented Generation (RAG) different from traditional LLMs?
Traditional LLMs only use data that has already been trained, which can lead to answers that are wrong or out of date. RAG makes LLMs better by getting real-time, external, and domain-specific knowledge. This makes sure that the information is accurate, based on facts, and can be used by businesses.
What is Retrieval-Augmented Generation (RAG)?
RAG is an AI framework that combines information retrieval with generative models. It enhances responses by fetching relevant external data before generating answers. TechPratham provides practical training on building RAG pipelines with real-world datasets.
Why is RAG important in AI?
RAG improves accuracy and reduces hallucinations in large language models by grounding answers in real data. It ensures reliability in enterprise use cases. TechPratham trains learners to implement RAG for business-critical applications.
How does RAG work technically?
RAG retrieves documents from a knowledge base using vector search, then feeds them into a generative model for context-aware answers. This creates fact-based responses. TechPratham explains these steps with hands-on labs and projects.
What are the main applications of RAG?
RAG is used in chatbots, enterprise search, legal research, healthcare support, and knowledge management. It powers AI systems with domain-specific accuracy. TechPratham covers these applications through industry-focused projects.
Do I need deep ML knowledge to implement RAG?
Basic knowledge of NLP, embeddings, and databases is helpful but not mandatory. Tools like LangChain and LlamaIndex simplify implementation. TechPratham offers beginner-to-advanced training so learners can build RAG systems step by step.
What tools and frameworks support RAG?
Popular tools include LangChain, LlamaIndex, Pinecone, Weaviate, and FAISS. They integrate with models like GPT, LLaMA, and Falcon. TechPratham provides structured training on these tools to help learners build scalable RAG solutions.
How does RAG differ from fine-tuning LLMs?
Fine-tuning modifies a model’s weights, while RAG augments it with external knowledge without retraining. This makes RAG more flexible and cost-effective. TechPratham ensures learners understand both approaches with comparison-based projects.
Can RAG be deployed in enterprises?
Yes, enterprises use RAG for secure knowledge retrieval, compliance support, and customer service. It integrates with private databases and APIs. TechPratham prepares learners to design enterprise-ready RAG solutions with security best practices.
What are the challenges of RAG?
Challenges include latency, retrieval quality, and maintaining up-to-date knowledge bases. Proper indexing and filtering are critical for accuracy. TechPratham teaches optimization techniques to overcome these challenges in production systems.
ERP Partners
Other Learn Ai Courses
📚
No Related Courses Found
We couldn't find any other courses in the "Learn Ai" category at the moment.
Registered Office:
Noida Office:
Hyderabad Office:
Trending Certification Courses
Company
© 2025 TechPratham. All rights reserved.An ISO 9001:2015 Certified Company