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10 Days Realtime LLM Bootcamp
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Introduction
Basics of LLM
Word Vectors Simplified
Prompt Engineering
Retrieval Augmented Generation and LLM Architecture
What is Retrieval Augmented Generation (RAG)?
Primer to RAG Functioning and LLM Architecture: Pre-trained and Fine-tuned LLMs
In-Context Learning
High level LLM Architecture Components for In-context Learning
Diving Deeper: LLM Architecture Components
LLM Architecture Diagram and Various Steps
RAG versus Fine-Tuning and Prompt Engineering
Versatility and Efficiency in Retrieval-Augmented Generation (RAG)
Key Benefits of RAG for Enterprise-Grade LLM Applications
Similarity Search in Vectors (Bonus Module)
Using kNN and LSH to Enhance Similarity Search in Vector Embeddings (Bonus Module)
Track your Progress
Hands-on Development
Live Interactions with Jan Chorowski and Adrian Kosowski | Bonus Resource
Final Project + Giveaways
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In-Context Learning

In this video, you'll be introduced to the concept of in-context learning through prompts. Anup explains how this form of learning is scalable, particularly when dealing with vast amounts of data.
This becomes especially relevant when we recall our earlier discussions on Retrieval Augmented Generation (RAG). Understanding in-context learning amplifies the efficacy of technologies like RAG in Large Language Models.
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Primer to RAG Functioning and LLM Architecture: Pre-trained and Fine-tuned LLMs
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High level LLM Architecture Components for In-context Learning
Last modified 17h ago