--- title: "LLM Observability: Eye On LLMs In Production" description: LLM Observability refers to the concept of observing and evaluating the performance and behavior of Language Model systems and ensuring its reliability. image: https://www.xenonstack.com/hubfs/top-llm-observability-tools.png --- - [![xenonstack-logo](https://www.xenonstack.com/hubfs/xenonstack-logo-new-relase.svg)](https://www.xenonstack.com/) - - Foundry Foundry Unified reasoning foundation enabling seamless orchestration, analytics, infrastructure, and trust across intelligent ecosystems [![pointers-icon](https://www.xenonstack.com/hubfs/xs-revamp-header-dropdown/our-purpose-line-icon.svg) Akira AI - Reasoning and Agent Orchestration Turn models into collaborative, policy-governed agents that learn and act together](https://www.xenonstack.com/agentic-platforms/akira-ai/) [![pointers-icon](https://www.xenonstack.com/hubfs/xs-revamp-header-dropdown/autonomous-operations-icon.svg) ElixirData - Agentic Analytics Intelligence Explainable, decision-centric analytics for measurable business outcomes](https://www.xenonstack.com/agentic-platforms/elixirdata/) [![pointers-icon](https://www.xenonstack.com/hubfs/xs-revamp-header-dropdown/digital-immune-system-icon.svg) NexaStack - 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Book Strategy Call ![slider-cross-icon](https://www.xenonstack.com/hubfs/slider-cross-icon.svg) ## Interested in Solving your Challenges with XenonStack Team ## Get Started Get Started with your requirements and primary focus, that will help us to make your solution First Name \* Please enter a valid First Name Last Name \* Please enter a valid Last Name Business Email ID \* Please enter a valid Business Email ID Contact Number \* Please enter a valid Contact Number Company \* Please enter a valid Company Name Industry Belongs To \* Please Select your Industry Banking Fintech Payment Providers Wealth Management Discrete Manufacturing Semiconductor Machinery Manufacturing / Automation Appliances / Electrical / Electronics Elevator Manufacturing Defense & Space Manufacturing Computers & Electronics / Industrial Machinery Motor Vehicle Manufacturing Food and Beverages Distillery & Wines Beverages Shipping Logistics Mobility (EV / Public Transport) Energy & Utilities Hospitality Digital Gaming Platforms SportsTech with AI Public Safety - 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Our XenonStack Team will shortly reach out to you. We are looking forward to showcase how XenonStack can transform your business. ![usecase-banner (1)](https://www.xenonstack.com/hs-fs/hubfs/usecase-banner%20(1).webp?width=1921&height=622&name=usecase-banner%20(1).webp) [Enterprise AI](https://www.xenonstack.com/blog/tag/enterprise-ai) # LLM Observability: Eye On LLMs In Production [Dr. Jagreet Kaur](https://www.xenonstack.com/blog/author/dr-jagreet-kaur) | 13 August 2024 ## The Necessity of Observability in LLM In today's digitally connected world, [Large Language Models (LLMs)](https://www.xenonstack.com/blog/large-language-models) have carved a niche for themselves by excelling in tasks such as text generation, language translation, and the creation of diverse forms of creative content. Despite their versatility and power, the complex nature of these models makes them challenging to manage and understand. This is where the critical role of observability for Large Language Models comes into play. - **Ensuring Reliability and Accuracy** One of the primary reasons for the rising importance of LLM observability is its capacity to bolster the model's reliability and accuracy. Real-time performance monitoring allows for the early detection of operational hitches or inaccuracies, making it possible to rectify issues before they escalate into larger problems affecting end-users. - **Enhancing Performance Metrics** Beyond just troubleshooting, observability grants an understanding of the model's operational nuances. This deeper comprehension can lead to identifying methods for improving not just its accuracy, but also its efficiency and scalability. In essence, observability acts as the diagnostic tool for fine-tuning Large Language Models, thereby enabling enhanced performance. - **Risk Mitigation** The third pillar that underlines the necessity for observability is risk management. LLMs are potent but they are not without their hazards. Whether it’s in generating text or other functions, the ability to pre-emptively identify and inhibit the production of harmful or inappropriate content is vital. Observability equips organizations with the tools to do just that, making it an indispensable aspect of responsible LLM deployment and management. ## What is Observability? [Observability](https://www.xenonstack.com/insights/what-is-observability) in the context of LLMs refers to the systematic practice of scrutinizing and comprehending the intricacies of a model's performance and behaviour. This scrutiny involves gathering critical information about various facets of the model, such as its input mechanisms, output results, and even its inner workings. The insights derived from this data can then be used for troubleshooting and performance optimization. By collecting data from the three pillars (Metrics, Logs, Traces), you can gain a comprehensive understanding of the model's behaviour. This information can be used to identify problems, such as: - The model is generating incorrect or misleading output. - The model is taking too long to generate output. - The model is crashing or producing errors. Observability can also be used to improve the performance of LLMs. By understanding how the model works, you can identify ways to improve its accuracy, efficiency, and scalability. Key aspects of LLM Observability ### Performance Tracking The bedrock of LLM Observability lies in consistent performance tracking. This process gathers pivotal metrics around the LLM's functionality—how accurate are its predictions, what is the response time, and what kind of errors are observed? These metrics not only highlight issues like biases but also pave the way for rectifications. Different techniques can be employed for performance tracking. Metrics like accuracy, precision, and recall remain popular choices. While accuracy gauges the proportion of correct predictions, precision evaluates the relevance of these predictions. Recall, on the other hand, quantifies how many relevant results are captured by the model. Logging is another method for performance tracking. It provides an in-depth log of model behaviour, including inputs, outputs, errors, and other [anomalies](https://www.forbes.com/councils/forbesagencycouncil/2020/02/03/how-to-apply-anomaly-detection-and-reap-these-three-benefits/). This granular information is instrumental in diagnosing issues with the LLM. ### In-Depth Comprehension Beyond tracking, gaining a deep understanding of LLMs is paramount. This entails scrutinizing the training data, elucidating the decision-making algorithms, and recognizing any limitations. Acquiring this knowledge makes it easier to pinpoint and rectify model-related issues. Understanding the distribution of training data is vital, as biases in the data can translate into model biases. Similarly, an analysis of the decision-making mechanics helps identify possible biases or inaccuracies within the model. Lastly, recognizing LLM limitations is invaluable. While these models are advanced, they are not infallible. They can exhibit biases, generate errors, and are susceptible to specific input anomalies. ### Accuracy Optimization A principal objective of LLM Observability is to enhance model accuracy, which involves identifying and mitigating biases and errors. Bias detection is one prevalent technique to maintain accuracy. This methodology uncovers systematic biases within the model's predictive outcomes. For instance, if a particular demographic is favoured by the model's predictions, it points to a bias. Error detection is another robust technique to safeguard accuracy. This approach flags any inaccuracies in the model's output, like incorrect categorizations. Once identified, biases and errors can be corrected through various measures like retraining the model on diverse datasets or fine-tuning model parameters. ### Reliability Assurance Ensuring LLM reliability is another cornerstone of Observability. The goal is to verify that the models are robust and can adapt to a wide array of input scenarios. Stress testing is a popular method for ascertaining reliability. It pushes the model to its limits by presenting a broad spectrum of inputs, especially those designed to challenge the model. A reliable model will navigate these scenarios without crashing or generating false outputs. Fault tolerance is another strategy to buttress reliability. This design principle allows the model to continue functioning smoothly even when certain components face failures. For example, a resilient model should still yield accurate predictions if one of its layers malfunctions. By meticulously tracking performance, enhancing understanding, optimizing accuracy, and assuring reliability, LLM Observability aims to make LLMs more dependable and trustworthy. ![observability-and-monitoring-icon](https://www.xenonstack.com/hubfs/observability-and-monitoring-icon.svg) **[O](https://hubs.ly/Q01t_g9Y0)[bservability](https://hubs.ly/Q01t_g9Y0) is the extent to which you can understand the internal state or condition of a complex system based only on knowledge of its external outputs.** ## Techniques for monitoring LLM ### Activity Logging Activity logging offers a systematic approach to capture critical data that sheds light on a model's functioning. It allows for real-time debugging and diagnostics. When monitoring LLMs, it's essential to focus on pivotal events. These could include the model's input and output data, error occurrences, or any unusual behaviour. Such information aids in isolating any complications like model biases or errors. What to log: - Input and output data - Response time latency - Error count - Any deviations from expected behaviour ### Performance Indicators Performance indicators or KPIs (Key Performance Indicators) offer numerical evaluations that provide insights into an LLM's effectiveness. Key metrics often used for assessing LLMs are accuracy, precision, and recall. These metrics allow you to gauge how well the model is performing, facilitating targeted improvements. For instance, a declining accuracy rate over a period could signal a growing bias within the model, demanding immediate action. ### Detecting Anomalies [Anomaly detection](https://www.xenonstack.com/use-cases/anomaly-detection) serves as a robust tool for recognizing aberrations or outliers in model behaviour, which could be indicative of biases or functional issues. The methodology behind anomaly detection is the identification of data patterns that deviate substantially from the norm. Such outlier data is tagged as anomalous. For instance, a sudden surge in the error rate could represent an anomaly and might imply that the model is malfunctioning or developing biases. Various techniques like statistical models or machine learning algorithms can be deployed for anomaly detection. ### Decision Interpretability Understanding the rationale behind a model's decisions is crucial, and explainability methods serve this purpose. These techniques can uncover biases in decision-making and help in fine-tuning the model for greater accuracy. These methods offer a window into a model’s reasoning pathway, either by tracking the computational steps it undergoes or by providing natural language explanations for its choices. For example, if an LLM is tasked with predicting someone’s gender, explainability methods can reveal the rationale behind the decision. This can help identify and rectify any existing biases. Decision interpretability techniques can thus significantly contribute to enhancing a model’s accuracy by identifying and rectifying biases. ## Tools for LLM Observability At this point of time, there are multiple tools that one can use for LLM Observability. Some of them are ### Arize This is one of the most popular LLM Observability tool out there. They allow you to monitor the LLM model’s prompts plus response embeddings performance. They also allow to automatically group data points that are similar (semantically) and sort them based on performance. ### WhyLabs This is also one of the major players when it comes to observability of the ML models. This platform provides quite a straightforward presentation of result, outlier detection, histograms, data drift monitoring and missing feature values. ### MLFlow It is an open-source platform that one can use to manage the lifecycle of a [ML model.](https://www.xenonstack.com/big-data-analytics/machine-learning/) They have four components: - MLflow Tracking - MLflow Projects - MLflow Models - Model Registry A [neural network](https://www.xenonstack.com/blog/artificial-neural-network-applications) is **a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain**. ## Challenges of LLM Observability ### Intricacy of LLM Architectures Large Language Models (LLMs) are inherently intricate, composed of multiple layers of interconnected neurons. Deciphering the interaction between these layers can pose significant challenges for observability, rendering monitoring a complex task. ### Absence of Universal Performance Indicators When it comes to LLM Observability, there's a glaring absence of universally accepted metrics. Organizations often employ varying measures such as accuracy, precision, or recall, making it strenuous to conduct comparative analyses or establish industry best practices. ### Data-Intensive Nature of LLM Effective observability for LLMs demands substantial data resources. These models require training on extensive datasets to ensure precise predictive capabilities. Organizations with limited access to large datasets may find this a daunting hurdle to overcome. ### Requirement for Specialized Skills The intricate nature of LLMs necessitates specialized expertise in Machine Iearning and [Artificial Intelligence](https://www.xenonstack.com/blog/artificial-intelligence). This skill gap can be a significant obstacle for organizations lacking the resources to onboard experts in the field. Additional Considerations for LLM Observability - **Financial Investment:** Implementing LLM Observability often necessitates considerable investment in specialized tools and resources. - **Time Constraints:** The practice is time-intensive, involving the collation and scrutiny of large volumes of data. - **Regulatory Compliance:** Organizations may also need to navigate legal frameworks that govern the usage of LLMs, adding an extra layer of complexity to observability efforts. ![Java vs Kotlin](https://www.xenonstack.com/hubfs/10-28-2022%20Digital%20Transformation_07%20Assessment.png) **A [large language model (LLM)](https://hubs.ly/Q01WK7YP0) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content.**  ## Best practices for LLM Observability ### Clarify the Purpose and Targets of Your Large Language Model The foundational step in enhancing LLM observability is crystal-clear comprehension of what the model aims to achieve. Grasping these objectives equips you with insights into which performance indicators should be at the forefront of your monitoring efforts. For instance, if your LLM is designed for text generation, you might focus on scrutinizing the quality, fluency, and range of the generated text. ### Pinpoint Crucial Performance Indicators for Your LLM Armed with a nuanced understanding of your LLM’s mission and goals, you can zero in on vital metrics to track. Such metrics serve as barometers for the model’s efficiency and can alert you to emerging issues. Commonly employed metrics in the Large Language Model landscape encompass elements like accuracy, precision, recall, and ethical fairness. ### Adopt Context-Specific Monitoring Solutions The realm of LLMs offers a diverse toolkit of monitoring methodologies, ranging from log capturing and metrics tracking to anomaly spotting and explicability features. The most suitable method varies depending on your specific LLM type and its application context. ### Gather and Dissect LLM-Generated Data After setting up tailored monitoring mechanisms, the next move is to amass and interpret the data emanating from your LLM. Data analytics can help pinpoint inefficiencies or areas of improvement, facilitating proactive troubleshooting. ### Detect Faults or Inconsistencies in LLM Performance By meticulously examining the [harvested data](https://www.ibm.com/docs/en/storediq/7.6.0?topic=server-data-harvesting), you can spotlight any operational hitches or inconsistencies that plague your Large Language Model. Such discrepancies may arise from numerous factors, including but not limited to, data biases, mis calibrated settings, or even hardware malfunctions. ### Implement Corrective Actions for Identified Issues Upon unearthing any performance hiccups, the next course of action involves remedial steps to rectify these challenges. Remediation might entail recalibrating the model, tweaking algorithmic parameters, or hardware-related troubleshooting. ### Engage in Ongoing LLM Supervision and Refinement Given the dynamic nature of LLMs, constant vigilance and real-time adjustments are pivotal for sustaining optimal performance. By continuously updating your monitoring strategy and making timely interventions, you bolster the reliability and credibility of your LLMs. By adhering to these optimized strategies for LLM observability, you not only enhance performance but also contribute to building trustworthy and reliable Large Language Models.   ## Conclusion One of the most important aspects of LLM observability is the ability to monitor, understand, and validate LLMs. By doing so, organizations can reduce the risks associated with the use of these powerful AI models. - What are [Large Language Models](https://www.xenonstack.com/blog/large-language-models#:~:text=A%20large%20language%20model%20\(LLM,collection%20of%20human%2Dgenerated%20text.)in Artificial Intelligence? - Generative AI use in[Healthcare Industry.](https://www.xenonstack.com/industries/digital-healthcare/) - Benefits of [Generative AI in Education Sector.](https://www.xenonstack.com/blog/generative-ai-education) ## Share Article - [![XenonStack Facebook](https://www.xenonstack.com/hubfs/xenonstack-facebook-service.svg)](http://www.facebook.com/share.php?u=https://www.xenonstack.com/blog/llm-observability-eye-on-llms-in-production) - [![XenonStack Twitter](https://www.xenonstack.com/hubfs/xs-twitter-white-updated-icon.svg)](https://twitter.com/intent/tweet?text=I+found+this+interesting+blog+post&url=https://www.xenonstack.com/blog/llm-observability-eye-on-llms-in-production) - [![XenonStack Linked In](https://www.xenonstack.com/hubfs/xenonstack-linkedin-service.svg)](http://www.linkedin.com/shareArticle?mini=true&url=https://www.xenonstack.com/blog/llm-observability-eye-on-llms-in-production) - [![XenonStack Email Icon](https://www.xenonstack.com/hubfs/xenonstack-email-service.svg)](mailto:?subject=Check%20out%20https://www.xenonstack.com/blog/llm-observability-eye-on-llms-in-production%20&body=Check%20out%20https://www.xenonstack.com/blog/llm-observability-eye-on-llms-in-production) ## Table of Contents ## Share Article - [![XenonStack Facebook](https://www.xenonstack.com/hubfs/xenonstack-facebook-service.svg)](http://www.facebook.com/share.php?u=https://www.xenonstack.com/blog/llm-observability-eye-on-llms-in-production) - [![XenonStack Twitter](https://www.xenonstack.com/hubfs/xs-twitter-white-updated-icon.svg)](https://twitter.com/intent/tweet?text=I+found+this+interesting+blog+post&url=https://www.xenonstack.com/blog/llm-observability-eye-on-llms-in-production) - [![XenonStack Linked In](https://www.xenonstack.com/hubfs/xenonstack-linkedin-service.svg)](http://www.linkedin.com/shareArticle?mini=true&url=https://www.xenonstack.com/blog/llm-observability-eye-on-llms-in-production) - [![XenonStack Email Icon](https://www.xenonstack.com/hubfs/xenonstack-email-service.svg)](mailto:?subject=Check%20out%20https://www.xenonstack.com/blog/llm-observability-eye-on-llms-in-production%20&body=Check%20out%20https://www.xenonstack.com/blog/llm-observability-eye-on-llms-in-production) ## Explore Related Topics [Decision Intelligence](https://www.xenonstack.com/blog/tag/decision-intelligence) [Cloud Native Applications](https://www.xenonstack.com/blog/tag/cloud-native-applications) [Generative AI](https://www.xenonstack.com/blog/tag/generative-ai) [Big Data Engineering](https://www.xenonstack.com/blog/tag/big-data-engineering) [FinOps](https://www.xenonstack.com/blog/tag/finops) [Data Foundry](https://www.xenonstack.com/blog/tag/data-foundry) [XAI](https://www.xenonstack.com/blog/tag/xai) [Autonomous Agents](https://www.xenonstack.com/blog/tag/autonomous-agents) [MetaSecure AI](https://www.xenonstack.com/blog/tag/metasecure-ai) ![dr-jagreet-gill](https://www.xenonstack.com/hubfs/dr-jagreet-gill-author.svg) ## Dr. Jagreet Kaur Chief Research Officer and Head of AI and Quantum Dr. Jagreet Kaur specializing in Generative AI for synthetic data, Conversational AI, and Intelligent Document Processing. With a focus on responsible AI frameworks, compliance, and data governance, she drives innovation and transparency in AI implementation [Explore More by Dr. Jagreet Kaur ![cta-blue-arrow](https://www.xenonstack.com/hubfs/cta-arrow-blue.svg)](https://www.xenonstack.com/blog/author/dr-jagreet-kaur-gill) ![Subscribe background](https://www.xenonstack.com/hubfs/blog-post-subscribe.svg) ## Subscribe to our Latest Technology Insights and Resources Subscribe Now ![slider-cross-icon](https://www.xenonstack.com/hubfs/slider-cross-icon.svg) ## Get the latest articles in your inbox Business Email ID \* Please enter a valid Business Email ID Company Name \* Please enter a valid Company Name Yes, I would like to receive the XenonStack newsletter as well as marketing emails regarding XenonStack products, services, and events. I can unsubscribe at any time. By registering, you confirm that you agree to the processing of your personal data by XenonStack as described in the Privacy Policy. 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This ensures faster detection, fewer incidents, and stronger reliability - ![Performance Icon](https://www.xenonstack.com/hubfs/performance.svg)Proactive detection of performance and availability issues - ![Root Cause Icon](https://www.xenonstack.com/hubfs/root-cause.svg)Root-cause analysis across microservices and environments - ![Remediation Icon](https://www.xenonstack.com/hubfs/remediation.svg)Automated remediation playbooks to reduce MTTR [Explore Agent SRE](https://agentsre.ai/) ![akira-ai-banner-illustration](https://www.xenonstack.com/hubfs/akira-ai-banner-illustration.svg) ✕ ## Physical Surveillance with Vision AI Agent Technology AI converts camera feeds into instant situational awareness. It detects unusual motion and unsafe behavior in real time. Long hours of video become searchable and summarized instantly - ![Motion Icon](https://www.xenonstack.com/hubfs/motion.svg)Real-time detection of suspicious motion or intrusion - ![Video Search Icon](https://www.xenonstack.com/hubfs/video-search.svg)Natural language video search and instant playback - ![Summary Icon](https://www.xenonstack.com/hubfs/summary.svg)Smart summaries for audits, investigations, and compliance [See Vision AI in Action](https://www.xenonstack.ai/) ![physical-surveillance](https://www.xenonstack.com/hubfs/xai-banner-image.svg) ✕ ## Agentic Data Intelligence Across Your Full Data Stack Your data stack becomes intelligent and conversational. Agents surface insights, detect anomalies, and explain trends. Move from dashboards to autonomous, always-on analytics - ![Connects warehouses](https://www.xenonstack.com/hubfs/data-icon.svg)Connects to warehouses, lakes, and streaming sources - ![Question Answering](https://www.xenonstack.com/hubfs/answers.svg)Question-answering in natural language - ![Continuous monitoring](https://www.xenonstack.com/hubfs/monitoring-2.svg)Continuous monitoring for anomalies and KPI deviations [See in Action](https://elixirdata.co/) ![agentic-data-intelligence](https://www.xenonstack.com/hubfs/empowerment-of-analysts.svg) ✕ ## Intelligent Diagnostic for Self-Healing System Automation Agents identify recurring failures and performance issues. They trigger workflows that resolve common problems automatically. Your infrastructure evolves into a self-healing environment - ![Diagnostics Icon](https://www.xenonstack.com/hubfs/diagnostic.svg)Automated diagnostics for recurring errors - ![Playbook Icon](https://www.xenonstack.com/hubfs/playbook.svg)Playbook execution: restart services, scale pods, clear queues - ![Feedback Icon](https://www.xenonstack.com/hubfs/feedback.svg)Feedback loop for improving remediation strategies [See in Action](https://agentanalyst.ai/) ![intelligent-diagnostic](https://www.xenonstack.com/hubfs/dataops-reimagined-banner-image.svg) ✕ ## Agentic GRC - Monitoring Risk and Compliance Controls AI continuously checks controls and compliance posture. It detects misconfigurations and risks before they escalate. Evidence collection becomes automatic and audit-ready - ![Controls Icon](https://www.xenonstack.com/hubfs/controls.svg)Continuous control checks across infrastructure and SaaS - ![Audit Icon](https://www.xenonstack.com/hubfs/audit.svg)Automated evidence collection for audits - ![Risk Icon](https://www.xenonstack.com/hubfs/risk.svg)Risk scoring and prioritized remediation recommendations [Explore Agent GRC](https://agentgrc.ai/) ![monitoring-risk-and-compliance](https://www.xenonstack.com/hubfs/enhanced-security-measures.svg) ✕ ## Agentic Finance and Procurement Intelligent Agents Financial and procurement workflows become proactive and insight-driven. Agents monitor spend, vendors, and contracts in real time. Approvals and sourcing decisions become faster and smarter - ![Visibility Icon](https://www.xenonstack.com/hubfs/visibility.svg)Real-time visibility into spend and commitments - ![Anomaly Icon](https://www.xenonstack.com/hubfs/anomaly.svg)Anomaly detection on invoices and vendor performance - ![Workflow Icon](https://www.xenonstack.com/hubfs/workflow.svg)Intelligent workflows for approvals and sourcing decisions [Optimize Finance & Procurement](https://www.xenonify.ai/) ![agentic-finance-and-procurement](https://www.xenonstack.com/hubfs/responsible-ai-aviators-banner-illustration.svg) ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Dr. Jagreet Kaur", "url" : "https://www.xenonstack.com/blog/author/dr-jagreet-kaur" }, "dateModified" : "2024-08-13T04:52:04.257Z", "datePublished" : "2023-10-18T10:36:34.000Z", "headline" : "LLM Observability: Eye On LLMs In Production", "image" : [ "https://www.xenonstack.com/hubfs/top-llm-observability-tools.png" ], "mainEntityOfPage" : { "@id" : "https://www.xenonstack.com/blog/llm-observability-eye-on-llms-in-production", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://www.xenonstack.com/hubfs/Xenonstack-Serives%20-3-2-2021-10.png" }, "name" : "Xenonstack Inc" } } ``` ```json { "@context" : "http://schema.org", "@type" : "Organization", "address" : { "@type" : "PostalAddress", "addressCountry" : "USA", "addressLocality" : "Plano", "addressRegion" : "Texas", "postalCode" : "75024", "streetAddress" : "7700 Windrose Ave. " }, "description" : "XenonStack is the #1 Data and AI foundry and technology services company to simplify and scale Enterprise and Generative AI Journeys.", "email" : "business@xenonstack.com", "logo" : "https://f.hubspotusercontent30.net/hubfs/8161231/Xenonstack-Serives%20-3-2-2021-10.png", "mainEntityOfPage" : { "@id" : "https://www.xenonstack.com/blog/llm-observability-eye-on-llms-in-production", "@type" : "WebPage", "description" : "LLM Observability refers to the concept of observing and evaluating the performance and behavior of Language Model systems and ensuring its reliability." }, "name" : "XenonStack", "sameAs" : [ "https://www.facebook.com/XenonStack", "https://www.linkedin.com/company/xenonstack/", "https://www.youtube.com/c/XenonStackOfficial", "https://twitter.com/xenonstack" ], "telephone" : "", "url" : "https://www.xenonstack.com/" } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Dr. Jagreet Kaur" }, "dateModified" : "August 13, 2024, 4:52:04 AM", "datePublished" : "2023-10-18 10:36:34", "description" : "LLM Observability refers to the concept of observing and evaluating the performance and behavior of Language Model systems and ensuring its reliability.", "headline" : "LLM Observability: Eye On LLMs In Production", "image" : { "@type" : "ImageObject", "url" : "https://www.xenonstack.com/hubfs/top-llm-observability-tools.png" }, "mainEntityOfPage" : { "@id" : "https://www.xenonstack.com/blog/llm-observability-eye-on-llms-in-production", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://f.hubspotusercontent30.net/hubfs/8161231/Xenonstack-Serives%20-3-2-2021-10.png" }, "name" : "XenonStack" } } ```