Table of Contents 1. What Is Observability in LLMs? A Detailed Overview A simple mental model Basic example 2. The Pillars of Observability 2.1 Logs: The Narrative 2.2 Metrics: The Health Indicators 2.3 Traces: The Story Across Services 2.4 New Pillars for LLMs: Evals & Feedback 3. Why Do We Need Observability for LLMs? 3.1 LLMs Fail Differently 3.2 Regulatory and Brand Risk 3.3 Cost and Performance 4. Why LLM Observability Matters at Scale 4.1 More Features, More Surprises 4

Table of Content Why RAG Is Essential for Reliable LLMs at Scale RAG Explained: Enhancing LLM Reliability Through Contextual Retrieval The RAG Framework: Core Components Inside the RAG Pipeline: Retrieval, Augmentation, and Generation Building Production-Grade RAG: Overcoming Challenges and Measuring What Matters Real-World Applications of RAG RAG vs. Fine-Tuning: Pick the Right Tool Conclusion: The Future of RAG Bibliography I've built and shipped several RAG systems in prod

Table of Content What Data Observability Means? The Five Pillars of Data Observability Why Do We Need Data Observability? Why Data Observability Matters at Scale? Leading Tools for Data Observability My Takeaway Bibliography In today's data-driven enterprises, data powers everything from strategic decisions to advanced analytics and AI innovations. As organizations scale, with complex pipelines, diverse data sources, and distributed teams, ensuring data reliability becomes in

Azure stream analytics job can read from various source eg. IOT, Eventhub, Blob Storage etc. ASA job can run on azure cloud and can also...

Azure key vault (AKV) service stores keys, passwords, certificates, etc., in a centralized storage. AKV mitigates the risk of exposure of confidential details such as database connection strings, passwords, private keys, etc., in source code which can cause undesired consequences. Access to a key vault requires proper authentication and authorization and with RBAC, teams can have even fine granular control who has what permissions over the sensitive data. Azure Databricks (AD

Window functions Window functions are a special group of functions that scan multiple input rows to compute each output value. Window functions are usually used with OVER, PARTITION BY, ORDER BY, and the windowing specification. Different from the regular aggregate functions used with the GROUP BY clause, and limited to one result value per group, window functions operate on windows where the input rows are ordered and grouped using flexible conditions expressed through an OV

Azure CosmosDB : Data Modeling and Migration Azure Cosmos DB is a schema-free databases and this feature makes Cosmos DB highly preferable to store and query unstructured and semi-structured data, but it requires deep understanding about application data model to get the most of the service in terms of performance and scalability and lowest cost. Before designing the data modeling we need to understand: How is data going to be stored? Data access pattern. How is application g