Once we accept that LLMs can sometimes hallucinate, we're left with other integration hurdles when incorporating them into apps:<p>- Inconsistent APIs across different LLMs<p>- Not entirely reliable<p>- Higher latencies<p>- The need to manage rate-limits ,downtimes, errors<p>To address these, I recommend starting with these 5 steps:<p>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.<p>2. Alerts for Failures: Be proactive. Set up alerts for both request and response level failures for swift issue resolution.<p>3. Eye on the Clock: Monitor API latencies closely. Opt for streaming, smaller models for simpler tasks, and parallel calls to boost performance.<p>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.<p>Captured more on this in the blog here: https://portkey.ai/blog/building-reliable-llm-apps/