The systems guide to production token optimization

The article discusses several strategies to optimize and reduce costs when using large language models (LLMs) for production applications, specifically focusing on two systems: Concierge, a customer support chatbot, and Pathfinder, an automated debugging assistant. Here are the key optimization techniques mentioned:

1. Token Bounding:

   • Use logit bias or schema constraints to limit the model's output to specific tokens.
   • Example: For Concierge, responses are constrained to be within a defined schema.

2. State Management:

   • Implement caching for static prompt prefixes and historical conversation segments that remain stable over multiple turns.
   • Example: Cache reads can reduce costs by up to 90% compared to full model invocations.

3. Semantic Caching:

   • Use vector similarity search to identify semantically similar queries and return cached responses instead of invoking the LLM.
   • Example: Redis is used as a cache layer for semantic caching, reducing latency significantly for duplicate requests.

4. Context Compaction:

   • Summarize or condense historical context when it grows too large, using smaller models to create summaries.
   • Example: Concierge keeps the last 3 turns verbatim and summarizes older history

Read the full article at The New Stack

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