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Context Window Scaling

Techniques for handling very long contexts in production LLM apps and when to use RAG vs long context

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Subtopics
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Exercises
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Projects
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Quiz Qs
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Flashcards
▶📚 Prerequisites(3)

🎓 Learning objectives

  • •Explain why context length scaling is quadratically expensive
  • •Describe RoPE scaling and how models extend context
  • •Know the lost-in-the-middle problem and its implications
  • •Choose between long context, RAG, and prompt caching for a given use case
  • •Calculate KV cache memory for a model configuration

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📂 Subtopics

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What Limits Context Windows: Attention Complexity, Memory, and Cost

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Positional Encodings for Long Context: RoPE and ALiBi

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Long Context Techniques: Sliding Window, Sparse Attention, and Retrieval-Augmented Approaches

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Practical Implications: Long Context vs. RAG, and Real Model Limits

Related concepts

llm-optimizationrag-workflowllm-foundations

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llm-safety-guardrails →
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