How I Cut Our AI Agent Token Costs by 73% Without Sacrificing Quality

Deploying AI agents can be incredibly powerful, but it often comes with significant costs due to token usage. By implementing several optimization strategies, you can drastically reduce these costs while maintaining or even improving the quality of your AI interactions. Here are four key strategies:

1. Prompt Compression

• Objective: Reduce the number of tokens used in each request.
• Implementation:
  • Use concise language and remove unnecessary details from prompts.
  • Employ token-efficient data structures like JSON for complex inputs.
  • Implement a prompt template system to reuse common phrases and patterns.

2. Caching

• Objective: Reduce the number of LLM calls by storing and reusing responses.
• Implementation:
  • Use an in-memory cache (e.g., Redis) or persistent storage for frequently requested queries.
  • Implement a caching mechanism that checks if a similar request has been made before sending it to the LLM.

Example Cache Implementation:

python
 
1import redis
2
3class PromptCache:
4    def __init__(self, host='localhost', port=6379):
5        self.cache = redis.Redis(host=host, port=port)
6
7    def get_response(self, prompt: str) ->
8
9[Read the full article at DEV Community](https://dev.to/rapidclaw/how-i-cut-our-ai-agent-token-costs-by-73-without-sacrificing-quality-31pn)
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