Your AI Agent Works. That's Why Finance Is About to Kill It.

Implementing prompt caching can significantly reduce costs by avoiding redundant computations for identical or similar prompts. Here’s a detailed approach to implementing prompt caching:

1. Understanding Prompt Caching

Prompt caching involves storing the results of expensive model calls (e.g., GPT-3, Claude) and reusing them when the same input is encountered again. This can drastically reduce costs by eliminating duplicate requests.

2. Choosing a Cache Implementation

You need to choose an appropriate cache implementation that fits your use case:

• In-Memory Caching: Fast but limited in size (e.g., Redis, Memcached).
• Distributed Caching: Scales better for larger applications (e.g., Redis Cluster, AWS ElastiCache).

3. Designing the Cache Key

The cache key should uniquely identify a prompt and its context:

python
 
1def generate_cache_key(prompt: str, context: dict) -> str:
2    # Combine prompt and relevant context into a unique string
3    return f"{prompt}:{json.dumps(context)}"

4. Implementing the Cache Wrapper

Create a wrapper around your model calls to handle caching logic:

python
 
1import json
2from typing import Dict
3
4[Read the full article at DEV Community](https://dev.to/shakti_mishra_308e9f36b5d/your-ai-agent-works-thats-why-finance-is-about-to-kill-it-19p5)
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