A Helping Hand for LLMs (Retrieval Augmented Generation) - Computerphile

More about Jane Street internships at: https://jane-st.co/internship-compute... (episode sponsor) Mike Pound discusses how Retrieval Augmented Generation can improve the performance of Large Language Models. Mike is based at the University of Nottingham's School of Computer Science.   / computerphile     / computer_phile   This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: https://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at https://www.bradyharanblog.com Thank you to Jane Street for their support of this channel. Learn more: https://www.janestreet.com

Join Today
Vector Search with LLMs - Computerphile
▶︎

Vector Search with LLMs - Computerphile

What is RAG? (Retrieval Augmented Generation)
▶︎

What is RAG? (Retrieval Augmented Generation)

How AI 'Understands' Images (CLIP) - Computerphile
▶︎

How AI 'Understands' Images (CLIP) - Computerphile

"Software Fundamentals Matter More Than Ever" — Matt Pocock
▶︎

"Software Fundamentals Matter More Than Ever" — Matt Pocock

Attention in transformers, step-by-step | Deep Learning Chapter 6
▶︎

Attention in transformers, step-by-step | Deep Learning Chapter 6

How Passkeys Work - Computerphile
▶︎

How Passkeys Work - Computerphile

Yann LeCun's $1B Bet Against LLMs [Part 1]
▶︎

Yann LeCun's $1B Bet Against LLMs [Part 1]

The Riskiest Moment of the AI Bubble
▶︎

The Riskiest Moment of the AI Bubble

Cloud Computing Explained: The Most Important Concepts To Know
▶︎

Cloud Computing Explained: The Most Important Concepts To Know

RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models
▶︎

RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models

Why AI Tokens are so Expensive - Computerphile
▶︎

Why AI Tokens are so Expensive - Computerphile

Transformers, the tech behind LLMs | Deep Learning Chapter 5
▶︎

Transformers, the tech behind LLMs | Deep Learning Chapter 5

Andrej Karpathy: From Vibe Coding to Agentic Engineering w/ Stephanie Zhan
▶︎

Andrej Karpathy: From Vibe Coding to Agentic Engineering w/ Stephanie Zhan

RAG vs. CAG: Solving Knowledge Gaps in AI Models
▶︎

RAG vs. CAG: Solving Knowledge Gaps in AI Models

Keynote: After the AI Hype – What’s Real, and What’s Next - Richard Campbell - 2026
▶︎

Keynote: After the AI Hype – What’s Real, and What’s Next - Richard Campbell - 2026

RAG Crash Course for Beginners
▶︎

RAG Crash Course for Beginners

System Design Explained: APIs, Databases, Caching, CDNs, Load Balancing & Production Infra
▶︎

System Design Explained: APIs, Databases, Caching, CDNs, Load Balancing & Production Infra

But how do AI images and videos actually work? | Guest video by Welch Labs
▶︎

But how do AI images and videos actually work? | Guest video by Welch Labs

Is RAG Still Needed? Choosing the Best Approach for LLMs
▶︎

Is RAG Still Needed? Choosing the Best Approach for LLMs

Andrej Karpathy: Software Is Changing (Again)
▶︎

Andrej Karpathy: Software Is Changing (Again)