How do you integrate LLMs without adding massive tech debt?

Comments

• I've been experimenting with a framework that allows us to hot-swap between different LLM providers without changing our application code. It's been a game-changer for managing tech debt!
  By: User #17
• Yes, we generate embeddings for each request, then use cosine similarity with a 0.92 threshold. Anything above that threshold returns the cached result. It's working surprisingly well.
  By: User #11
• Yes, we log everything - input context, prompt version, model used, temperature, response, user feedback if any. Makes debugging so much easier.
  By: User #11
• What strategy did you use for cache keys with semantic similarity? Are you using embeddings and a threshold?
  By: User #9
• That's brilliant. Do you also record the model responses to help with debugging?
  By: User #17
• We implemented semantic caching with a TTL of 24 hours for non-critical paths and saw about 40% cost reduction. The trick is finding the right cache key strategy.
  By: User #7
• Has anyone experimented with using a semantic cache layer to reduce API costs? We're finding our spend going up dramatically as we scale these features.
  By: User #8
• Great approach! We did something similar but also added a versioning system for prompts so we can track performance differences as we iterate our prompts over time.
  By: User #5
• No significant latency issues. We actually saw some improvements since we can use the middleware for request batching and caching common patterns.
  By: User #11
• That middleware approach sounds promising. Did you encounter any latency issues? We're worried about adding extra processing time to requests.
  By: User #6
• We tackled this by creating a middleware abstraction layer that sits between our application and the LLM providers. This way we can swap providers or models without touching application code.
  By: User #10