Fiddler Trust Service for LLM Application Scoring and Monitoring
Challenges and Risks in Deploying LLMs
Generative AI empowers enterprises to automate processes, streamline cost efficiencies, and improve customer and employee satisfaction. However, deploying LLMs for chatbots, content summarization, or other use cases comes with challenges such as hallucinations, toxicity, PII leakage, and prompt injection attacks, which can compromise the integrity of trustworthy and responsible AI.
LLMs work with unstructured data like text, where accuracy is deeply tied to context. A response that fits well in one situation might be inaccurate in another. Therefore, monitoring the quality, accuracy, and safety of LLM outputs requires advanced techniques that extend beyond standard metrics.
Fiddler Trust Service Simplifies the Complexities of LLM Monitoring
As part of the Fiddler AI Observability platform, the Fiddler Trust Service is a solution that enables data science, app development, and engineering teams to monitor LLM prompts and responses in production environments.
Powering the Fiddler Trust Service are proprietary, fine-tuned Fiddler Trust Models, designed for task-specific, high accuracy scoring of LLM prompts and responses with low latency. These models are built to handle higher traffic and inferences as LLM deployments scale, ensuring data protection in all environments — including air gapped deployments — and offering a cost-effective alternative to closed sourced models.
Fiddler Trust Models enable both offline diagnostics and real-time run-path guardrails for GenAI use cases by scoring various trust-related dimensions in prompts and responses. These Trust Scores detect for hallucination, toxicity, PII leakage, or prompt injection attacks, among other critical LLM metrics.
Fiddler Trust Models are Fast, Cost-Effective, and Accurate
1.5x
6x
50%
Comprehensive LLM Metrics Scoring
With the Fiddler Trust Service, you can score an extensive set of metrics, ensuring your LLM applications deliver the most advanced LLM use cases and stringent business demands.
- Faithfulness / Groundedness
- Answer relevance
- Context relevance
- Groundedness
- Conciseness
- Coherence
- PII
- Toxicity
- Jailbreak
- Sentiment
- Profanity
- Regex match
- Topic
- Banned keywords
- Language detection
Advanced Diagnostics for Improving LLM Applications
When the Fiddler Trust Models detect prompts and responses for issues like faithfulness, toxicity, jailbreaking attempts, or other concerns, AI teams can:
Score LLM Metrics with Precision
- The Fiddler Trust Service, powered by Trust Models, scores trust-related dimensions to detect LLM issues such as hallucinations, toxicity, and prompt injection attacks.
- Fiddler Trust Models are fast, secure, scalable, and cost-effective, designed to meet the most advanced enterprise LLM use cases.
Identify the Root Cause of LLM Issues
- Fiddler’s 3D UMAP visualization enables in-depth data exploration.
- Isolate problematic prompts and responses that show hallucinated, toxic, and jailbreaking content.
- Overlay segments and apply filters to gain deeper insights on LLM issues.
Improve LLM Applications with Actionable Insights
- Use Fiddler’s Slice and Explain to uncover the full set of flagged prompts and responses within a specific time period.
- Share this list with Model Development and Application teams to review and enhance the LLM application, preventing future issues.
Gain Visibility into LLM Applications
- Get comprehensive visibility into your LLM applications with customized dashboards and reports.
- Track the key LLM metrics that matter most for your use case and stakeholders, driving business-critical KPIs.