Evaluation and Observability for LLM Apps

Practical deep dive with open-access.

Monitoring and debugging LLMs is necessary but tricky and tedious.

We published a practical guide to integrate evaluation and observability into your LLM Apps with implementation.

It has open access to all readers.

Read it here: ​A Practical Guide to Integrate Evaluation and Observability into LLM Apps.​

We used Opik, an open-source, production-ready end-to-end LLM evaluation platform that allows developers to test their LLM applications in development, before a release (CI/CD), and in production.

Here are some key features:

• Record and understand the LLM response generation process.

• Compare many LLM responses in a user-friendly table.

• Log traces during LLM development and production.

• Use built-in LLM judges to detect hallucinations.

• Test the LLM pipeline with different prompts.

• Use its pre-configured evaluation pipeline.

​Opik is fully compatible with most LLMs and LLM development frameworks—OpenAI, Pinecone, LlamaIndex, Pinecone, you name it.

The deep dive is completely beginner-friendly and covers every piece of implementation.

Read it here: ​A Practical Guide to Integrate Evaluation and Observability into LLM Apps [OPEN-ACCESS].

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P.S. For those wanting to develop “Industry ML” expertise:

At the end of the day, all businesses care about impact. That’s it!

• Can you reduce costs?

• Drive revenue?

• Can you scale ML models?

• Predict trends before they happen?

We have discussed several other topics (with implementations) in the past that align with such topics.

Here are some of them:

• Learn sophisticated graph architectures and how to train them on graph data: A Crash Course on Graph Neural Networks – Part 1.

• So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here: Bi-encoders and Cross-encoders for Sentence Pair Similarity Scoring – Part 1.

• Learn techniques to run large models on small devices: Quantization: Optimize ML Models to Run Them on Tiny Hardware.

• Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust: Conformal Predictions: Build Confidence in Your ML Model’s Predictions.

• Learn how to identify causal relationships and answer business questions: A Crash Course on Causality – Part 1

• Learn how to scale ML model training: A Practical Guide to Scaling ML Model Training.

• Learn techniques to reliably roll out new models in production: 5 Must-Know Ways to Test ML Models in Production (Implementation Included)

• Learn how to build privacy-first ML systems: Federated Learning: A Critical Step Towards Privacy-Preserving Machine Learning.

• Learn how to compress ML models and reduce costs: Model Compression: A Critical Step Towards Efficient Machine Learning.

All these resources will help you cultivate key skills that businesses and companies care about the most.