Publication Specifications
FORMATDRM-Free PDF
LENGTH37 Pages
TIERadvanced Edition
PUBLISHERBlizarra Ltd
Advanced37 pages • Instant Download
Context Engineering & Memory Architectures
Long-context retrieval, Graph RAG, entity extraction, and episodic agent memory.
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About this Book
Long context windows do not solve the needle-in-a-haystack problem. Learn how context rot, attention dilution, and positional bias degrade output quality. Build sophisticated memory systems using knowledge graphs, temporal discounting, and associative vector storage.
Target Audience: Senior AI architects designing stateful, long-running agent workflows.
What You Will Learn & Build
Overcome the "lost in the middle" phenomenon in 1M+ token context windows
Construct dynamic Knowledge Graphs using Neo4j and entity-relationship extractors
Implement hierarchical memory consolidation modeled after human cognitive decay
Filter context dynamically to keep prompts laser-focused on pertinent facts
Table of Contents
1. Attention Degradation Across Extended Context WindowsSection 1
2. The Mechanics of Graph RAG: Entities, Relations & Community DetectionSection 2
3. Episodic vs Semantic vs Procedural Memory in Autonomous AgentsSection 3
4. Temporal Weighting: Decay Curves and Importance ScoringSection 4
5. Real-Time Memory Compaction & Hierarchical SummarizationSection 5
6. Benchmark Results on Complex Cross-Document SynthesisSection 6
7. Multi-Agent Shared Memory & Conflict ResolutionSection 7
8. Privacy, Forgetting & Memory Lifecycle ManagementSection 8