Observability: An Essential Block for Gen AI Solutions
Observability is more than just a buzzword; it is a critical component for any Gen AI-based solution. It offers visibility into usage, quality, costs, feedback, latency, and more.
Most importantly observability in Gen AI solutions is a precursor to effective evaluation (the focus of our next blog entry). Evaluating the ROI and value of Gen AI solutions is essential and to do that you need high quality usage data, feedback, and a mix of other manual and automated evaluation metrics. But to even get there — it is essential to gain an understanding of observability in the context of Gen AI. The needs for observing Gen AI solutions is quite different from traditional observability tooling (think APM tools like Datadog and New Relic). Gen AI applications require a tailored approach to observability.
Let us dive into the importance, features, and tools of observability in the context of Gen AI solutions.
Why Observability?
Observability enables:
- Tracing
- Debugging
- Monitoring
- Feedback
- Datasets Management
- Evaluations
- Conversational Sessions
- Prompt Management
Without observability, debugging applications, monitoring costs, tracking LLM latency, managing user prompts, and even performing evaluations become challenging. Observability is crucial throughout the Gen AI application lifecycle, from PoC to post-production support.
What is Observability in Gen AI?
Observability provides insight into Gen AI applications, helping understand their behavior, performance, and user interactions. It ensures transparency across various stages, from development to deployment.
Different Observability Tools
Over the years, Gen AI observability tooling has evolved significantly. Few popular tools include LangSmith, Prompt Layer, Weights & Biases, Helicone, Langfuse, etc. Among them, LangSmith and Langfuse stand out for their mature and feature-rich capabilities.
GenAI Application Integration with Observability Tool
Langfuse captures various elements for the Gen AI application using constructs such as traces, sessions, generations, etc., as illustrated below.
Below are building blocks of the Langfuse SDK, sourced from the link.
- A Trace represents a single execution of an LLM feature, serving as a container for all subsequent objects.
- Each Trace can include multiple Observations to document individual execution steps.
— Events are fundamental building blocks used to track discrete actions within a Trace.
— Spans record time intervals and include an end time.
— Generations are Spans tracking AI model iterations with added metadata, displayed uniquely in Langfuse UI.
Langfuse offers a straightforward method for integrating applications with its tool. It provides out-of-the-box (OOTB) Python/JS SDKs designed to seamlessly integrate with popular GenAI frameworks and libraries such as OpenAI, Langchain, Llamaindex, Flowise, and Langflow.
Langfuse’s standalone SDK is user-friendly, offering simple APIs for creating traces, observations, events, spans, and generations.
Here is the method used to log traces and key elements into the Langfuse tool, as depicted below
Observability Tool in SmartPal
SmartPal, Cybage’s internal LLM based knowledge management application, benefits from Langfuse’s robust observability capabilities. We realized the need for strong observability tooling early on in our Gen AI journey and ensure we ironed out all the kinks in SmartPal before helping our customers integrate their own observability tooling.
Dashboard: It offers a summary or overview of activities, metrics, or statistics concerning SmartPal’s usage or performance.
Sessions: A session captures the entire conversation of a user.
Traces: These are logs or records of activities and actions performed within SmartPal.
Here is the image detailing the trace:
Generations: This refers to capturing an actual LLM call, including the model’s name, input/output tokens, and the associated cost.
Scores: This gathers feedback from users based on the information retrieved by SmartPal.
Users: This captures all user information, including token usage, feedback, and API hits.
Datasets: This feature would enable users to manage datasets used for testing or reference within SmartPal.
Evaluation: The Evaluation offers insights into Gen AI model performance and user satisfaction metrics for continuous improvement. We will dive deeper into evaluation in the next blog.
Prompt Management: Prompt Management allows for the creation, modification, and organization of prompts to ensure consistent and effective interactions with Gen AI models. Prompt Management / versioning and iteration is another major topic that deserves its dedicated focus but observability tooling can be the central hub for these untraditional needs of managing prompt sets effectively.
Data Privacy & Security in Observability Tooling
Data privacy and security are crucial considerations, especially for enterprises utilizing these tools based on the use case. Given that these tools gather user prompts, knowledge base context, and responses, there is a potential for the collected data to include personally identifiable information (PII) or other confidential details. Depending on the use case, this data could adhere to various privacy frameworks such as GDPR, SOC2, ISO 27001, HIPAA, and more. Therefore, it is essential to thoroughly assess the data privacy and security features of any tool before selecting or implementing it for a Gen AI-based solution. Here are the data privacy and security compliance details for Langfuse.
Conclusion
In conclusion, observability practices are essential for enhancing the performance and user experience of LLM tools such as SmartPal. By adopting tools like Langfuse, organizations can fully leverage the capabilities of their AI initiatives, fostering innovation, efficiency, and success in today’s competitive environment. They can also start thinking deeply about evaluation — what does it mean to have a succesful Gen AI feature in production? How should this be calculated? These are questions every organization is facing and observability of experiments is a cornerstone to that question.
Gen AI Competency Center: Unlock the Potential
In a constantly evolving AI landscape, understanding and leveraging observability tools are crucial steps toward achieving organizational goals. With our expertise in observability and Gen AI, we help organizations fully harness the power of AI, delivering unparalleled innovation and efficiency.
Are you looking to supercharge your Gen AI solutions? Cybage’s Gen AI Competency Center offers innovative capabilities to drive your AI projects to success. Whether you are just beginning your AI journey or looking to enhance existing projects, we are here to guide and support you every step of the way. Contact us today to start your transformative AI journey with Cybage.
