Context Engineering Knowledge Graph

Clear progression from understanding limits to active management strategies. Token budgeting requires understanding tokenization first.

Clear skill progression from basic prompting to structured XML prompts to chain-of-thought to meta-prompting. Each level builds on prior techniques.

KV cache mechanics are well-defined. Prefix stability has clear rules. Provider implementations follow documented specifications.

Clear pipeline stages: chunking → embedding → indexing → retrieval → reranking → injection. Each stage has well-defined inputs and outputs.

Compression techniques have clear trade-offs but optimal strategy depends heavily on task. Some techniques are combinable, others exclusive.

Embedding models, distance metrics, and index types have clear hierarchies. But optimal configuration is workload-dependent.

Rapidly evolving field. Patterns exist but best practices shift frequently. Multiple valid architectural approaches for similar problems.

Multiple valid approaches (sliding window, summarization, hybrid). Optimal strategy depends heavily on use case and conversation patterns.

Metrics are well-defined but interpretation is context-dependent. What constitutes "good" context efficiency varies by application.