ranbot-ai/context-window-management

Context Window Management

Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot

Capabilities

• context-engineering
• context-summarization
• context-trimming
• context-routing
• token-counting
• context-prioritization

Prerequisites

• Knowledge: LLM fundamentals, Tokenization basics, Prompt engineering
• Skills_recommended: prompt-engineering

Scope

• Does_not_cover: RAG implementation details, Model fine-tuning, Embedding models
• Boundaries: Focus is context optimization, Covers strategies not specific implementations

Ecosystem

Primary_tools

• tiktoken - OpenAI's tokenizer for counting tokens
• LangChain - Framework with context management utilities
• Claude API - 200K+ context with caching support

Patterns

Tiered Context Strategy

Different strategies based on context size

When to use: Building any multi-turn conversation system

interface ContextTier { maxTokens: number; strategy: 'full' | 'summarize' | 'rag'; model: string; }

const TIERS: ContextTier[] = [ { maxTokens: 8000, strategy: 'full', model: 'claude-3-haiku' }, { maxTokens: 32000, strategy: 'full', model: 'claude-3-5-sonnet' }, { maxTokens: 100000, strategy: 'summarize', model: 'claude-3-5-sonnet' }, { maxTokens: Infinity, strategy: 'rag', model: 'claude-3-5-sonnet' } ];

async function selectStrategy(messages: Message[]): ContextTier { const tokens = await countTokens(messages);

for (const tier of TIERS) {
    if (tokens <= tier.maxTokens) {
        return tier;
    }
}
return TIERS[TIERS.length - 1];

}

async function prepareContext(messages: Message[]): PreparedContext { const tier = await selectStrategy(messages);

switch (tier.strategy) {
    case 'full':
        return { messages, model: tier.model };

    case 'summarize':
        const summary = await summarizeOldMessages(messages);
        return { messages: [summary, ...recentMessages(messages)], model: tier.model };

    case 'rag':
        const relevant = await retrieveRelevant(messages);
        return { messages: [...relevant, ...recentMessages(messages)], model: tier.model };
}

}

Serial Position Optimization

Place important content at start and end

When to use: Constructing prompts with significant context

// LLMs weight beginning and end more heavily // Structure prompts to leverage this

function buildOptimalPrompt(components: { systemPrompt: string; criticalContext: string; conversationHistory: Message[]; currentQuery: string; }): string { // START: System instructions (always first) const parts = [components.systemPrompt];

// CRITICAL CONTEXT: Right after system (high primacy)
if (components.criticalContext) {
    parts.push(`## Key Context\n${components.criticalContext}`);
}

// MIDDLE: Conversation history (lower weight)
// Summarize if long, keep recent messages full
const history = components.conversationHistory;
if (history.length > 10) {
    const oldSummary = summarize(history.slice(0, -5));
    const recent = history.slice(-5);
    parts.push(`## Earlier Conversation (Summary)\n${oldSummary}`);
    parts.push(`## Recent Messages\n${formatMessages(recent)}`);
} else {
    parts.push(`## Conversation\n${formatMessages(history)}`);
}

// END: Current query (high recency)
// Restate critical requirements here
parts.push(`## Current Request\n${components.currentQuery}`);

// FINAL: Reminder of key constraints
parts.push(`Remember: ${extractKeyConstraints(components.systemPrompt)}`);

return parts.join('\n\n');

}

Intelligent Summarization

Summarize by importance, not just recency

When to use: Context exceeds optimal size

interface MessageWithMetadata extends Message { importance: number; // 0-1 score hasCriticalInfo: boolean; // User preferences, decisions referenced: boolean; // Was this referenced later? }

async function smartSummarize( messages: MessageWithMetadata[], targetTokens: number ): Message[] { // Sort by importance, preserve order for tied scores const sorted = [...messages].sort((a, b) => (b.importance + (b.hasCriticalInfo ? 0.5 : 0) + (b.referenced ? 0.3 : 0)) - (a.importance + (a.hasCriticalInfo ? 0.5 : 0) + (a.referenced ? 0.3 : 0)) );

const keep: Message[] = [];
const summarizePool: Message[] = [];
let currentTokens = 0;

for (const msg of sorted) {
    const msgTokens = await countTokens([msg]);
    if (currentTokens + msgTokens < targetTokens * 0.7) {
        keep.push(msg);
        currentTokens += msgTokens;
    } else {
        summarizePool.push(msg);
    }
}

// Summarize the low-importance messages
if (summarizePool.length > 0) {
    const summary = await llm.complete(`
        Summarize these messages,