Overcoming Challenges with LLM APIs

Once we accept that LLMs can sometimes hallucinate, we're left with other integration hurdles when incorporating them into apps:

- Inconsistent APIs across different LLMs

- Not entirely reliable

- Higher latencies

- The need to manage rate-limits ,downtimes, errors

To address these, I recommend starting with these 5 steps:

1. Log and Analyse: Ensure you're logging all requests and responses. If you're dealing with a lot of text data, consider a specialized logging tool to prevent costs from spiraling.

2. Alerts for Failures: Be proactive. Set up alerts for both request and response level failures for swift issue resolution.

3. Eye on the Clock: Monitor API latencies closely. Opt for streaming, smaller models for simpler tasks, and parallel calls to boost performance.

4. Navigating Rate Limits: Don't be hampered by HTTP 429 errors. Implement rate limit handling on both the LLM provider's side and on the user's end for a smoother experience.

Captured more on this in the blog here: https://portkey.ai/blog/building-reliable-llm-apps/