Token Savings
Definition: Strategies and architectural patterns designed to reduce the number of Token processed by llm, thereby lowering API Cost and improving latency.
Key Mechanisms
- Context Window Optimization: Pruning irrelevant historical data and summarizing prior interactions to minimize input size.
- Prompt Engineering: Using concise instructions and few-shot examples to reduce redundancy in system prompts.
- Caching & Memoization: Reusing responses for identical or similar inputs to avoid re-computation.
- Persistent Memory Systems: Offloading long-term context to external databases rather than maintaining it in the active context window.
- Local Agent Configuration Tuning: Adjusting core settings for local assistants (e.g., Hermes AI) to balance context limits, output constraints, and memory usage, as detailed in Optimizing Hermes AI Assistant Configuration for Context, Output, and Memory Limits.
Implementation Case: OpenCode & Claude-Mem
- Problem: [[concepts/ai-coding-agents|AI coding age