LLM apps need careful watching. Here's how to do it right:

• •Track key metrics: Accuracy, latency, cost, user engagement
• •Set up alerts: Choose metrics, set thresholds, plan actions
• •Check data quality: Define standards, use tools, monitor over time
• •Test for security: Use OWASP Top 10, control access, watch logs
• •Pick an LLM monitoring platform: Keywords AI, Langsmith.

Why it matters:

• •Keeps AI apps reliable and safe
• •Catches mistakes and biases
• •Saves money on resources
• •Protects user data

Remember: LLM monitoring is ongoing. Keep learning and improving your process.

Keeping your LLM app running smoothly? It's all about watching the right numbers. Here's what you need to track:

Accuracy: Is your LLM getting it right? This is key for quality.

Latency: Speed counts. Watch these:

• •Time to First Token (TTFT)
• •Time Per Output Token (TPOT)

Cost: LLM requests can hit your wallet hard. Some might set you back $1-5 each.

User Engagement: Are folks actually using your LLM?

Alert systems are crucial for your LLM app. They help you catch issues fast and get user feedback.

Here's how to set up effective alerts:

• •Pick your metrics

Choose what to track based on your app's goals. This might include accuracy, latency, or user engagement.

• •Set thresholds

Decide when an alert should trigger. For example:

Choose alert channels

Pick how you'll get notified. Options include Slack, email, SMS, or PagerDuty.

Create an action plan

Know what to do when an alert fires. This might mean checking data quality, adjusting model parameters, or pausing the service for fixes.

Gather user feedback

Set up ways for users to report issues or give input. This could be through in-app feedback forms, user surveys, or support ticket analysis.

Use anomaly detection

Spot weird patterns that might signal problems. Tools like Edge Delta can help with this.

Test your system

Make sure alerts work as planned. Run drills to check response times and processes.

Keep improving your alert system. What you track today might not be what you need tomorrow.

Data quality can make or break your LLM app. Bad data? Bad results. Simple as that.

Here's how to keep your data clean:

Set quality standards What's "good data" for your app? Define it. Example:

Use LLM evaluation framework

Frameworks like Relari and Ragas can run evaluations on your LLM responses and your RAG content. You can self host those frameworks or choose a provide to automatically log your LLM requests and run the evaluations, like Keywords AI.

Monitor key metrics

Track data quality over time. Watch for drops.

Export good data to a dataset.

Simply export those good data to a dataset, you could feed your LLM with those golden datasets.

LLM apps can be a hacker's playground. You need to test for security issues regularly.

Here's how:

Use the OWASP Top 10 for LLMs

OWASP lists the top 10 security risks for LLM apps:

You need specialized tools to keep your LLM apps running smoothly. Here are some top options:

Keywords AI: Built for AI startups. Features include: