Prev Next

AI / LLM Basics Interview Questions

What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation combines a language model with an external retrieval step, pulling in relevant documents or data before generating a response, rather than relying purely on what the model memorized during training.

  • A retrieval step searches an external knowledge source for content relevant to the current query
  • That retrieved content is added into the model's context before it generates its answer
  • Helps reduce hallucination by grounding responses in retrievable, verifiable source material

RAG is especially useful for keeping answers current or specific to private data, since it lets a model reference information well beyond whatever it happened to see during pretraining.

What happens before generation in a RAG pipeline?
What does RAG help with by grounding answers in retrieved material?

Invest now in Acorns!!! 🚀 Join Acorns and get your $5 bonus!
Acorns Logo

Invest now in Acorns!!! 🚀
Join Acorns and get your $5 bonus!

Earn passively and while sleeping

Acorns is a micro-investing app that automatically invests your "spare change" from daily purchases into diversified, expert-built portfolios of ETFs. It is designed for beginners, allowing you to start investing with as little as $5. The service automates saving and investing. Disclosure: I may receive a referral bonus.

Robinhood Logo

Invest now!!! Get Free equity stock (US, UK only)!

Use Robinhood app to invest in stocks. It is safe and secure. Use the Referral link to claim your free stock when you sign up!.

The Robinhood app makes it easy to trade stocks, crypto and more.


Webull Logo

Webull! Receive free stock by signing up using the link: Webull signup.

More Related questions...

What is a Large Language Model (LLM)? What are Tokens in an LLM? What is Tokenization? What is Byte-Pair Encoding (BPE)? What is the Vocabulary of an LLM? What is an Embedding in the context of LLMs? Define the Transformer architecture? What is the Attention Mechanism? What is Self-Attention? What is Multi-Head Attention? What is Positional Encoding? What is the purpose of the Feed-Forward Network inside a Transformer block? What is Layer Normalization? What is Causal Masking? Describe the role of Softmax in an LLM's output layer? What is Cross-Entropy Loss? What is the purpose of a Loss Function during LLM training? What is Pretraining in the context of LLMs? What is Fine-Tuning? What is Instruction Tuning? Describe Reinforcement Learning from Human Feedback (RLHF)? What is Alignment in the context of LLMs? Define In-Context Learning? Define Zero-Shot Learning? Define Few-Shot Learning? What is Chain-of-Thought Prompting? What is Prompt Engineering? What is the purpose of a System Prompt? What is a Context Window? What is the Max Tokens parameter? What are Stop Sequences? What is Temperature in LLM sampling? What are Top-k and Top-p Sampling? What is Hallucination in LLMs? What is Perplexity in language modeling? What is Overfitting in the context of training an LLM? What are Parameters in an LLM? What is Quantization? What is Model Distillation? What is a Foundation Model? What is a Decoder-Only model? What is an Encoder-Only model? What is an Encoder-Decoder model? What is a Mixture of Experts (MoE) architecture? What is Retrieval-Augmented Generation (RAG)? How are Embeddings used beyond text generation? What are Guardrails in LLM applications? What is Prompt Injection?
Show more question and Answers...

Database

Comments & Discussions