LLM Observability
The most robust observability solution for large language models (LLMs) in production. Evaluate LLM responses, pinpoint where to improve with prompt engineering, and identify fine-tuning opportunities using vector similarity search.
Troubleshoot LLM apps with LLM Tracing
Gain insight into the inner workings of your LLM apps with LLM tracing on telemetry data.
Pinpoint the specific traces or spans of the application that contribute to sub-optimal performance to efficiently debug execution issues.
Detect Problematic Prompts & Responses
LLMs can hallucinate, provide incorrect information or suboptimal responses, incur bad retrieval, among many things that can go wrong.
Monitor your model’s prompt/response embeddings performance. Using LLM evaluation scores and clustering analysis, Arize helps you narrow in on areas your LLM needs improvement.
Analyze Clusters Using LLM Evaluation Metrics & GPT-4
Automatically generate clusters of semantically similar datapoints and sort by performance. Arize supports LLM-assisted evaluation metrics, task-specific metrics, along with user feedback.
An integration with ChatGPT enables you to analyze your clusters for deeper insights.
Improve Your LLM Responses with Prompt Engineering
Pinpoint prompt/response clusters with low evaluation scores.
Generative workflows suggest ways to augment prompts to help your LLM models generate better responses.
Fine-Tune Your LLM Using Vector Similarity Search
Find problematic clusters, such as inaccurate or unhelpful responses, to fine-tune with better data.
Vector-similarity search clues you into other examples of issues emerging, so you can begin data augmentation before they become systemic.
Pre-Built Clusters for Prescriptive Analysis
Use pre-built global clusters identified by Arize algorithms, or define custom clusters of your own to simplify RCA and make prescriptive improvements to your generative models.
