Company Case Study
Evaluating and Monitoring an Enterprise LLM Application
LLM
E-commerce, EdTech, Finance, SaaS, Health
Client Profile
RagaAI’s client is a leading enterprise that recently adopted an LLM (Language and Learning Model) application to process and interact with their extensive repository of enterprise data. They faced challenges in evaluating and monitoring the performance of their LLM application. To address these concerns, RagaAI LLM offering was employed as a comprehensive testing solution.
Challenge
Our client, faced critical challenges with their LLM application: tackling incorrect or misleading answers that risk misinformation, navigating the complex selection of a model fit for their specific data needs, balancing response time with cost for operational efficiency, ensuring rigorous answer verification to address biases or inconsistencies, and developing a reliable fallback plan for instances of inaccurate LLM responses. These issues present a multifaceted and pressing challenge for the company.
Solution
RagaAI's Testing Platform, tailored for LLM applications, provided a holistic evaluation of the client's models. At the core of this solution was RagaAI DNA, a proprietary foundational model meticulously tuned for evaluating LLMs. This specialised model developed as RagaAI's intellectual property, became the linchpin for resolving challenges unique to the LLM domain.
Approaches
Hallucination Detection
RagaAI brings explainability to responses by identifying the source of the answer
Any deviation from the facts present in the context of the RAG application gets identified by RagaAI.
Latency and Cost Monitoring
Continuous monitoring of latency and cost of api responses
Actionable recommendations like caching, prompt compression gets implemented.
Prompt and Response Monitoring
Addressed data drift in production environments with prompt monitoring and determining clusters of prompt based on performance
Mitigated data drift, by improving prompt suggestions to the end user.
Model Selection Support
Auto-reroute of prompt to best model at backed that can be OpenAI s api or on- prep deployed llama.
Achieved cost and performance optimisation.
Fallback Strategy Implementation
Helped establish a reliable fallback mechanism to handle challenging scenarios when the LLM application faced difficulties.
Boosted performance in LLMs reliability in post deployment scenario.
At a Glance
Challenges
Poor performance of LLM in post-production environment and rising cost and latency challenges.
Solutions
RagaAI testing platform for comprehensive model evaluation
RagaAI DNA, a specialised foundational model for LLM testing applications.
Results
65% Initial Model Accuracy
97% Post-deployment Model Accuracy
Conclusion
Transformation:
RagaAI's solutions helped bring LLM application from lab to production
Key Components:
Hallucination Detection, Latency and Cost Monitoring, Prompt and Response Monitoring,Model Selection Support, Fallback Strategy Implementation.
Gains Achieved:
Reliability in LLM performance in post-production environment.
Cost-Effective:
RagaAI emerged as a cost-effective alternative to in-house development
Excellence Commitment:
Reinforced RagaAI's LLM Testing offerings at scale.
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