mediar-ai Context Optimization Techniques
Extend effective context capacity through strategic compression, masking, caching, and partitioning.
Prerequisites
- Understanding of context windows
- Familiarity with token economics
Instructions
Optimization Strategies
Compaction: Summarize context near limits, reinitialize with summary.
- Target 50-70% token reduction
- Less than 5% quality degradation
Observation Masking: Replace verbose tool outputs with references.
- Tool outputs can be 80%+ of token usage
- Store full output, return summary + reference
KV-Cache Optimization: Reuse cached computations for common prefixes.
- Place stable elements first (system prompt, tool definitions)
- Avoid dynamic content like timestamps
Context Partitioning: Split work across sub-agents with isolated contexts.
- Each sub-agent gets fresh context
- Aggregate results at coordination layer
Compaction Priority
- Tool outputs → replace with summaries
- Old turns → summarize early conversation
- Retrieved docs → summarize if recent versions exist
- Never compress → system prompt
Observation Masking Strategy
Never mask: Critical observations, most recent turn, active reasoning
Consider masking: 3+ turns ago, verbose outputs with extractable key points
Always mask: Repeated outputs, boilerplate, already-summarized content
Cache-Friendly Ordering
context = [system_prompt, tool_definitions] # Stable, cacheable
context += [reused_templates] # Reusable
context += [unique_content] # Unique per request
Guidelines
- Measure before optimizing—know your current state
- Apply compaction before masking when possible
- Design for cache stability with consistent prompts
- Partition before context becomes problematic
- Balance token savings against quality preservation
Notes
- Context quality matters more than quantity
- Optimization can double or triple effective capacity
- Monitor token utilization and trigger at 70-80%
Source: muratcankoylan/Agent-Skills-for-Context-Engineering