Best LLM Artificial Intelligence Course | at Visualpath
                     How LLMs Handle Reasoning & 
Context
• Overview of reasoning and contextual 
understanding in Large Language Models 
(LLMs).
Introduction to LLM Reasoning
• LLMs are AI models trained on massive text data.
• They understand, generate, and reason with language.
• Used in chatbots, assistants, analytics, and automation.
What Is Reasoning in LLMs?
• Reasoning means analyzing information and drawing 
conclusions.
• LLMs predict next words based on learned patterns.
• This creates an illusion of logical thinking.
Understanding Context
• Context includes surrounding text and conversation 
history.
• Attention mechanisms help track relationships.
• Ensures relevance across long conversations.
Transformer Architecture
• Transformers use self-attention.
• They process all words simultaneously.
• Helps understand long-range dependencies.
Tokenization & Embeddings
• Text is split into tokens.
• Tokens are converted into vectors (embeddings).
• Captures meaning, similarity, and intent.
Chain-of-Thought Reasoning
• LLMs generate intermediate reasoning steps.
• Improves accuracy in math and logic tasks.
• Enhances transparency of responses.
Context Window & Limitations
• Context window defines memory size.
• No true long-term memory.
• May hallucinate or make logical errors.
Conclusion & Key Takeaways
• LLMs use attention and probabilities for reasoning.
• Powerful but not truly intelligent.
• Best used with human oversight.
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