Learning plan cover

LLM personalization and memory

LEARNING PLAN

LLM personalization and memory

This learning plan is essential for AI engineers, ML practitioners, and developers who want to move beyond basic LLM usage to create truly intelligent, personalized applications. As businesses demand AI systems that understand context, remember user preferences, and adapt over time, the ability to implement memory systems and personalization techniques has become a critical competitive advantage in the AI space.

ByCommunity User
2 h 37 m
10 coursesUpdated 4 days ago
What Is ChatGPT Doing ... and Why Does It Work?LLM Fundamentals: Attention Is All You NeedAll About LLMs: The AI Revolution Explained10 sources

How This Learning Plan Was Made

This plan was crafted by BeFreed’s proprietary AI to help you learn LLM personalization and memory with ease. It is curated from in-depth research on the topic and structured around the most effective learning journeys proven by BeFreed users.

Each episode delivers bite-sized, high-impact lessons distilled from world-class sources — bestselling books, research papers, and expert insights. Together, they form a sophisticated yet accessible path to mastering LLM personalization and memory.

What You'll Learn

  • Master the architecture of large language models, including transformers, attention mechanisms, and how they generate text probabilistically
  • Design sophisticated prompts and contexts that transform generic AI responses into personalized, expert-level interactions
  • Implement memory systems using RAG and vector databases to give LLMs long-term recall while reducing costs
  • Apply parameter-efficient fine-tuning techniques to adapt models for specific domains without massive computational resources
  • Build production-ready adaptive AI systems that learn from user interactions and evolve over time
  • Create personalized AI experiences that maintain context, remember preferences, and deliver consistently relevant responses

Master the fundamentals of how large language models work, from transformer architecture to attention mechanisms and probabilistic text generation.

What Is ChatGPT Doing ... and Why Does It Work? cover
What Is ChatGPT Doing ... and Why Does It Work?

In-depth analysis of ChatGPT's AI mechanisms and effectiveness

9 m
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source 1source 2source 3source 4
6 sources
LLM Fundamentals: Attention Is All You Need

Deep dive into how ChatGPT and large language models actually work, from the revolutionary attention mechanism to probabilistic text generation. Perfect for understanding the core concepts behind modern AI.

9 m
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Guide to Large Language Models (LLMs): Capabilities and ApplicationsA Survey of Large Language Models: Evolution, Architectures, Adaptation, Benchmarking, Applications, Challenges, and Societal ImplicationsLarge language models: an overview of foundational architectures, recent trends, and a new taxonomysource 4
6 sources
All About LLMs: The AI Revolution Explained

Lena and Eli break down everything about Large Language Models - from how ChatGPT predicts words to transformer architecture, real-world applications, and why understanding LLMs is essential for the future.

10 m

Learn to craft effective prompts and contexts that transform generic LLM responses into expert-level, personalized interactions tailored to specific needs.

Artificial Intelligence and Generative AI for Beginners cover
Artificial Intelligence and Generative AI for Beginners

Comprehensive guide to AI and generative AI for all skill levels.

9 m
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source 1source 2source 3Context Engineering for Large Language Models: A Tutorial
6 sources
Mastering Context Engineering for LLMs

Discover how to transform AI interactions from generic responses to expert-level collaboration through strategic context engineering. Learn the art of crafting conversational environments that unlock the true potential of language models.

24 m

Discover how to give LLMs long-term memory through Retrieval-Augmented Generation and vector databases, boosting accuracy while cutting costs dramatically.

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What is RAG? - Retrieval-Augmented Generation AI Explained - AWSWhat is Retrieval Augmented Generation (RAG)? - DatabricksRAG vs Traditional LLMs: Key Differences - Galileo AIIntroduction to RAG (Retrieval Augmented Generation) and Vector ...
6 sources
RAG vs LLMs: The AI Revolution Explained

Deep dive into Retrieval-Augmented Generation and vector databases - discover how RAG transforms AI accuracy by 13%, cuts costs 20x, and why it's replacing traditional LLMs in enterprise applications.

20 m
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source 1RAG Architecture: Complete Guide to Retrieval-Augmented ...Reference Architecture : Retrieval Augmented Generation (RAG)Optimizing Chunking, Embedding, and Vectorization for Retrieval ...
6 sources
RAG & Vector Databases: Cloud Architecture Deep Dive

Master RAG implementation from chunking strategies to Kubernetes deployment patterns. Explore vector database selection, optimization techniques, and production-ready cloud architectures for enterprise AI systems.

23 m

Master parameter-efficient fine-tuning, domain adaptation, and production strategies to create truly personalized AI experiences that learn and evolve with users.

Hooked cover
Hooked

How tech companies create habit-forming products using the Hook Model.

9 m
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A Survey on Post-training of Large Language ModelsFine-tuning large language models for domain adaptation - NatureFine-tuning and Utilization Methods of Domain-specific LLMsPEFT, LoRA & QLoRA: Smarter, Faster Fine-Tuning for Domain LLMs
6 sources
AI Post-Training for Real-World Applications

Discover how businesses are transforming AI models for specific needs through parameter-efficient fine-tuning, domain adaptation, and practical implementation strategies that deliver measurable results.

10 m
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source 1source 2Create Custom Podcasts Tailored to Your Interests | Airout AIContxt - Personalized AI generated Podcasts
6 sources
AI Podcast Revolution: Your Personal Audio Future

Discover how AI is creating completely personalized podcast experiences tailored to your exact interests, learning style, and preferences. From custom content generation to intelligent recommendations, explore the cutting-edge technology transforming podcasting into intimate, individualized audio ex

34 m

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