Module Overview
This module frames context as a managed resource. It covers how the context window behaves, how retrieval is really a context-construction problem, the memory architectures that persist information across turns and sessions, and the compression techniques that keep context within budget.
Learning Objectives
- Explain context window anatomy and recency bias.
- Treat RAG as context construction and choose chunking for context quality.
- Distinguish short-term, episodic, semantic, and procedural memory.
- Apply context compression and pruning with LLMLingua and RECOMP.
- Manage multi-turn conversation state and context accumulation.
Topics Covered
Context Window Anatomy & RAG as Context Construction
- Anatomy of a context window (tokens, ordering, recency bias)
- Retrieval-augmented generation as context construction
- Chunking strategies and their impact on context quality
Memory Architectures & Structured Context
- Memory architectures (short-term, episodic, semantic, procedural)
- Tool results and structured data as context
Context Compression, Pruning & Multi-Turn State
- Context compression and pruning (LLMLingua, RECOMP)
- Multi-turn conversation state and context accumulation
Key Concepts & Terminology
Recency/lost-in-the-middle effects, context budget, memory tiers, prompt compression, context pruning, conversation state accumulation.
Tools & Frameworks Referenced
LLMLingua, RECOMP, memory stores for agent state.
Prerequisites
Modules 16-17 (RAG) and Module 22 (prompt engineering).