Question 1

How is Retrieval-Augmented Generation (RAG) different from traditional LLMs?

Accepted Answer

Traditional LLMs only use data that has already been trained, which can lead to answers that are wrong or out of date.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.

Question 2

What is Retrieval-Augmented Generation (RAG)?

Accepted Answer

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.

Question 3

Why is RAG important in AI?

Accepted Answer

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.

Question 4

How does RAG work technically?

Accepted Answer

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.

Question 5

What are the main applications of RAG?

Accepted Answer

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.

Question 6

Do I need deep ML knowledge to implement RAG?

Accepted Answer

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.

Question 7

What tools and frameworks support RAG?

Accepted Answer

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.

Question 8

How does RAG differ from fine-tuning LLMs?

Accepted Answer

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.

Question 9

Can RAG be deployed in enterprises?

Accepted Answer

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.

Question 10

What are the challenges of RAG?

Accepted Answer

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.

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