AI Token Optimization Audit

ai-token-optimization · v1.1.0 · paid

Token management and cost-efficiency patterns to prevent unexpected API bills, covering context growth, token limits, and efficient streaming and caching implementation.

Context Management

context-management · weight 0.3

Context window utilization is tracked and surfaced
ab-000309
critical
Conversation history is truncated or summarized for long sessions
ab-000310
critical
System prompt token count is reasonable
ab-000311
high
Token counting is performed before API calls to prevent hard limit errors
ab-000312
high
A context compaction or summarization strategy is implemented for long sessions
ab-000313
medium
Maximum context window is explicitly configured per model
ab-000314
low

Token Efficiency

token-efficiency · weight 0.25

Response max_tokens is explicitly set on all AI API calls
ab-000315
critical
Smaller models are used for simple tasks (model routing by complexity)
ab-000316
high
Prompt templates minimize boilerplate tokens
ab-000317
medium
Embedding model dimensions are appropriate for the use case
ab-000318
medium
RAG chunk sizes are tuned for retrieval quality vs. token cost
ab-000319
info

Caching & Cost

caching-cost · weight 0.25

Repeated or identical AI queries are cached
ab-000320
high
Semantic or fuzzy caching is implemented for similar queries
ab-000321
medium
Token usage is logged per request for cost monitoring
ab-000322
high
Per-request cost estimation is computed and available for monitoring
ab-000323
low
Batch processing is used for non-interactive AI tasks
ab-000324
info

Streaming & Performance

streaming-performance · weight 0.2

Streaming is enabled for long AI responses
ab-000325
high
An abort/cancel mechanism exists for in-flight AI requests
ab-000326
high
Retry logic uses exponential backoff
ab-000327
medium
Client-side token counting validates user input before submission
ab-000328
low
Streaming responses use incremental rendering in the UI
ab-000329
info