Context Window Utilization

The Problem

Cannot monitor how efficiently the LLM context window is used, leading to wasted tokens, truncation, or suboptimal retrieval configurations.

Symptoms

• ❌ Don't know % of context window used

• ❌ Frequent context overflow unexplained

• ❌ Wasteful token usage

• ❌ Cannot optimize K parameter

• ❌ No visibility into token budget

Real-World Example

Configuration:
→ LLM: GPT-4 (8K context)
→ Retrieval: K=10 chunks
→ Chunk size: ~500 tokens each

Observed:
→ Context overflow errors: 15% of queries

Investigation:
→ System prompt: 300 tokens
→ User query: 100 tokens average
→ Retrieved context: 10 × 500 = 5,000 tokens
→ Response budget: 1,000 tokens
→ Total: 6,400 tokens (fits in 8K)

Why overflows?
→ No monitoring of actual token usage
→ Some chunks larger than 500 tokens (outliers)
→ Some queries longer (max: 800 tokens)
→ Total occasionally exceeds 8K

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Deep Technical Analysis

Token Accounting

Component Breakdown:

Total context (8,000 tokens):
1. System prompt: 300 tokens (fixed)
2. Conversation history: 0-2,000 tokens (variable)
3. Retrieved context: 2,000-6,000 tokens (variable)
4. Current query: 50-500 tokens (variable)
5. Response generation: 500-2,000 tokens (reserved)

Monitor each component:
→ Which consumes most?
→ Where to optimize?

Utilization Percentage:

Metric: Context utilization
= (used_tokens / max_tokens) × 100%

Examples:
→ Query A: 6,400 / 8,000 = 80% (good)
→ Query B: 8,500 / 8,000 = 106% (overflow!)
→ Query C: 3,200 / 8,000 = 40% (underutilized)

Target: 70-85% utilization
→ Below 70%: Retrieving too few chunks
→ Above 85%: Risk of overflow

Dynamic K Adjustment

Token-Based K Selection:

Instead of fixed K=10:
→ Retrieve chunks until token budget nearly full

Algorithm:
1. Calculate available: 8,000 - system - query - response_buffer = 5,500 tokens
2. Retrieve chunks sequentially:
   - Chunk 1: 450 tokens (total: 450)
   - Chunk 2: 520 tokens (total: 970)
   - ...
   - Chunk 11: 480 tokens (total: 5,520 → exceeds 5,500)
3. Stop at Chunk 10

Adaptive K based on token budget

Query Complexity Adaptation:

Simple query: "What is X?"
→ Short query (30 tokens)
→ More budget for context
→ K=15 possible

Complex query: "Explain how X, Y, and Z interact..."
→ Long query (200 tokens)
→ Less budget for context
→ K=8

Dynamic based on query length

Truncation Strategy Monitoring

Where Truncation Happens:

Monitor truncation point:
→ Truncated at chunk #8 of 10
→ Lost chunks 9-10

Were important chunks lost?
→ Chunk #9 score: 0.72 (relevant)
→ Chunk #10 score: 0.68 (marginal)

Impact: Moderate (lost one relevant chunk)

Sliding Window Stats:

Track truncation patterns:
→ 5% of queries: Truncate at chunk 5-7
→ 10% of queries: Truncate at chunk 8-10
→ 85% of queries: No truncation

Optimize:
→ Reduce K for shorter queries
→ Increase response buffer

Optimization Opportunities

Token Waste Detection:

Query uses 40% of context:
→ Only 3,200 / 8,000 tokens
→ Underutilized

Could increase K:
→ From K=5 to K=8
→ More context for better answers

Compression Opportunities:

System prompt: 300 tokens
→ Can compress to 200 tokens?
→ Saves 100 tokens
→ More for context

Conversation history: 1,500 tokens
→ Summarize to 500 tokens?
→ Saves 1,000 tokens

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How to Solve

Log token usage per component (system prompt, query, context, response) + calculate utilization % (used/max) + monitor truncation frequency and position + implement dynamic K based on available token budget + alert on high utilization (>85%) or overflow + track token distribution across queries + optimize underutilized queries (increase K) + compress system prompt or conversation history if needed. See Token Utilization.