Yugesh Marahatta

  RAG (Retrieval-Augmented Generation): How It Makes AI Smarter and More Accurate Large Language Models (LLMs) like GPT are powerful, but they have a limitation—they only know what they were trained on. If you ask about very recent events or company-specific data, they may respond inaccurately. This is where Retrieval-Augmented Generation (RAG) comes in. What Is RAG? RAG is an AI framework that combines: Retrieval → Searching external data sources (databases, documents, APIs). Generation → Using an LLM to generate responses based on retrieved information. Instead of relying purely on pre-trained knowledge, the AI “looks up” fresh, relevant data before responding. Why It Matters 📅 Up-to-Date Knowledge : Pulls the latest data instead of outdated training. 📂 Domain-Specific Answers : Works with private datasets (e.g., company documents, research papers). ✅ Reduced Hallucination : Less likely to make up facts, since it grounds answers in real data. 💡 Cus...

  Serverless Edge Computing: Bringing Functions Closer to Your Users Serverless computing has revolutionized the way developers create and implement apps during the past ten years. It was a developer favorite since it had a pay-per-use approach, automatic scaling, and no servers to maintain. However, a new development is currently taking place— serverless at the edge. What Is Serverless Edge Computing? Conventional serverless technologies, such as Google Cloud Functions and AWS Lambda, operate in centralized data centers. Despite their efficiency, they may have latency issues, particularly when customers are located far from the cloud region. Serverless edge computing solves this by deploying functions on edge locations —mini data centers distributed worldwide, closer to users. Think Cloudflare Workers, Vercel Edge Functions, Netlify Edge —they run your logic not in one region, but across dozens of edge nodes. Why It Matters ⚡ Ultra-Low Latency : Responses come from the n...

Serverless vs Containers: Key Differences Explained Although they address different issues, serverless functions and containers both aid in the deployment of apps without the need for conventional servers. Serverless Functions: Run individual tasks/functions Event-driven, short-lived Fully managed by cloud provider Pay-per-use pricing Containers: Package apps with dependencies Run continuously You manage deployment (or use orchestration like Kubernetes) Fixed-cost infrastructure Comparison Table Feature Serverless Containers Lifecycle Short-lived Long-running Management Cloud handles infra You manage infra Scaling Automatic per request Manual or orchestrated Use Case Event-driven, small apps Complex apps, microservices Cost Pay-per-use Fixed Example: Serverless: A Lambda function res...

How Serverless Functions Work Behind the Scenes Although serverless functionalities make development easier, the inner workings of the system are fascinating. Let’s uncover the magic. 1. Event Triggering: Serverless functions execute in response to an event, such as an HTTP request, database change, or file upload. 2. Cold Starts vs Warm Starts: Cold Start: When a function runs after being idle, cloud spins up a new container → slight delay. Warm Start: Already-running containers handle new requests → faster response. 3. Stateless Execution: Functions are stateless: no data is preserved between invocations. Persistent storage must be external (database, S3, etc.). 4. Scaling: Each function invocation is independent. If 100 requests arrive simultaneously, the cloud spins up 100 instances automatically. 5. Cost Model: You pay only for execution time and resources used, making it cost-efficient for intermittent workloads Conclusion: Understanding what happens behind the scenes helps d...

Serverless vs Traditional Backend The decision between a serverless and a traditional backend might affect development time, scalability, and cost. Let's investigate the best times for each. Serverless is Best When: You have intermittent workloads (sporadic traffic). You want rapid development and less infrastructure management. You need automatic scaling for unpredictable traffic. Examples: image processing, scheduled tasks, webhook handlers. Traditional Backend is Best When: Long-running processes are required. You need full control over infrastructure and servers. Low latency is critical for all requests. Examples: gaming servers, high-performance APIs, real-time data pipelines. Comparison Table: Feature Serverless Traditional Backend Infrastructure Managed by cloud Managed by you Scaling Automatic Manual/Configured Pricing ...

  Serverless Functions - A Beginner’s Guide Introduction: The way developers create and distribute applications is evolving due to serverless features.  Serverless servers, as opposed to typical servers, let you run code without having to worry about maintaining the supporting infrastructure. What is serverless function? A serverless function is a piece of code that runs in the cloud, triggered by events, and managed by a third-party provider, eliminating the need for developers to provision or manage servers. It's a way to execute code on demand without managing the underlying infrastructure. Cloud providers take care of provisioning, maintenance, and scaling.  AWS Lambda, Google Cloud Functions, and Azure Functions are a few examples. How Serverless Functions Work: Event-driven: functions trigger on HTTP requests, file uploads, database changes, etc. Short-lived: functions run for a limited time and then stop. Scalable automatically based on demand. Benefits: No server ...