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Articles by Damir Karimov
Scaling RAG Systems: Production Architecture, Performance, and Cost Optimization
The first three parts of this series covered why production RAG systems fail and how the quality of the data foundation directly affects everything that comes after it. We looked at document ingestion, parsing, chunking, and metadata design—the layers responsible for turning raw information into something a retrieval system can actually work with. Then we moved into retrieval itself.
Beyond Vector Search: Building Better RAG Retrieval with Hybrid Search and Reranking
The first two parts of this series covered why production RAG systems fail and how the quality of the data foundation directly affects everything that comes after it. We looked at document ingestion, parsing, chunking, and metadata design—the layers responsible for turning raw information into something a retrieval system can actually work with. But even perfectly processed documents are useless if retrieval cannot find the right information.
Building a Production RAG Pipeline: Document Processing, Chunking, and Metadata Design
In the first article, we explored why many RAG systems fail in production and established a key principle: retrieval quality determines answer quality. We also introduced the architecture behind production-grade RAG systems and explained why a simple "embeddings + vector database + LLM" pipeline is rarely enough. In this second part, we'll move one step earlier in the pipeline—to the moment a document first enters your system.
Why Most RAG Systems Fail in Production: The Hidden Architecture Problems Behind AI Search
Building a reliable RAG system is not about connecting an LLM to a vector database. It requires a complete architecture covering data ingestion, retrieval, ranking, evaluation, and production operations. This article is the first part of a five-part series where we will explore how modern RAG systems are designed, why naive implementations fail, and what it takes to build AI search systems that work reliably in production.
Stop Starting with Microservices (Build a Monolith First)
Most startups don't fail because they chose a monolith. They fail because they never built something people actually want. The "Microservices from Day One" Trap If you hang out on YouTube, Reddit, or X, you've probably seen this take: "Serious applications use microservices." It sounds right. It sounds modern. It sounds like what Netflix, Uber, and Amazon are doing. So teams copy the architecture… before they even have 100 users.
Why MCP Is Becoming the Standard Layer for AI Integrations (And Why Your Team Will Eventually Use It)
AI doesn't need another model. It needs a sane, reusable way to connect models to the real world: tools, data, APIs, and workflows. That's exactly the problem the Model Context Protocol (MCP) is solving. Right now, every AI product team is quietly rebuilding the same thing: a GitHub wrapper a Jira wrapper a Slack client a DB adapter a Drive integration The model is fine. The integration layer is chaos. And that chaos is exactly why MCP is becoming the standard layer for AI integrations.
Why Your Background Jobs Fail in Production
Most developers first meet background jobs through a deceptively simple model: put work in a queue, let a worker pick it up, and assume the job will be processed once. In production, that mental model breaks quickly because queue systems are designed for reliability under failure, not for “perfect” one-shot execution. Need to send an email? Queue it. Generate a PDF? Queue it. Process images? Queue it. That feels elegant at first.
Designing a Real-Time Chat System at Scale
Most developers underestimate how hard messaging systems really are. A basic chat demo with WebSockets is easy to build. A production-grade messaging platform like WhatsApp, Telegram, Discord, or Slack is a completely different engineering problem. The hard part is not rendering messages in the UI.
CI/CD for Modern Applications: From Manual Deployments to Reliable Delivery Pipelines
Last year my team had a production incident because someone manually deployed without running tests. The fix took 47 minutes. That's when I realized: CI/CD isn't about automation — it's about controlling risk in software delivery.
Real-Time Notification Systems Are Harder Than Most Teams Expect
If you’ve ever thought, “It’s just a WebSocket event,” this article is for you. Notification systems look simple on the surface, but in production they fail in annoying, expensive, and user-visible ways: duplicate notifications missing events race conditions delayed delivery mobile disconnects retry storms ordering bugs state drift across regions The tricky part is not sending a message.
Why Good Abstractions Make Debugging Harder
Good abstractions are great when you are building software. They are much less great when you are debugging production. The reason is simple: abstraction hides details, and debugging often depends on the details you hoped to ignore. In small codebases, this is barely noticeable. In real systems, especially with caches, async flows, optimistic UI, and multiple state owners, it becomes a serious problem.
AI-generated code doesn't fail loudly. It fails correctly-looking.
AI-generated code rarely breaks in obvious ways. It passes review, ships to production, and behaves correctly in controlled scenarios. The problem is what happens after: failures appear only under timing, load, retries, or inconsistent state transitions. The core issue is not obvious bugs. It is code that looks structurally correct while silently ignoring real-world failure modes.
LLM-Driven Client-Side Caching: A Hybrid Decision Architecture
Client-side caching is usually implemented as a storage optimization layer (TTL, SWR, invalidation rules). In practice it behaves like a decision system under uncertainty. Static strategies fail when data volatility is non-uniform across the same application. This leads to either stale UI or excessive network traffic.
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