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10 articles

Advanced RAG Architecture: Beyond Simple Vector Search

Advanced RAG: The Production Pipeline Simple Retrieval-Augmented Generation (RAG) is easy to build but hard to make accurate. To move beyond a basic prototype, you need an advanced architecture that optimizes every step…

Apr 20, 20262 min read

Deep Dive

#ai#llm#rag

DatabasesAdvanced

Vector Search in NoSQL: Redis and MongoDB as Vector Databases

Vector Search in NoSQL: The AI Evolution With the rise of Large Language Models (LLMs), Vector Search has become a critical requirement for Retrieval-Augmented Generation (RAG). While specialized databases like Pinecone…

Apr 20, 20262 min read

Deep Dive

#redis#mongodb#vector-search

AI/MLAdvanced

LLM Inference Optimization: Quantization, KV Cache, and High-Throughput Serving

The Real Cost of Running LLMs A 70B-parameter model in fp16 requires roughly 140 GB of GPU VRAM just for weights. That means two 80 GB accelerator cards before you serve a single token. Add KV cache for concurrent users…

Apr 8, 202614 min read

Deep Dive

#llm#inference#quantization

AI/MLAdvanced

LLM Observability in Production: Traces, Evals, Cost, Latency, and Failure Modes

LLM systems fail differently from normal backend systems. A normal service usually fails with a timeout, an exception, or a bad status code. An LLM application can return a fluent answer that is wrong, unsupported by ret…

Apr 8, 202611 min read

Deep Dive

#llm#observability#evals

AI/MLAdvanced

MCP for Backend Engineers: Tools, Agents, and Production Guardrails

MCP, the Model Context Protocol, is useful because it gives AI applications a standard way to connect to tools and context. For backend engineers, the interesting part is not the hype around agents. The interesting part…

Apr 8, 202614 min read

Deep Dive

#mcp#ai agents#tools

AI/MLIntermediate

Using Open Source Models with Claude Code: A Developer's Guide

Modern AI development doesn't have to mean sending all your code to proprietary APIs. With Claude Code and open source models, you can enjoy the power of AI-assisted development while keeping your data local and private.…

Mar 22, 20268 min read

Deep Dive

#claude code#open source#llm

AI/MLAdvanced

Fine-Tuning LLMs: When to Fine-Tune, When to Prompt

Fine-tuning is often the wrong choice. Most problems that engineers reach for fine-tuning to solve are better solved with better prompt engineering, few-shot examples, or RAG. But when you genuinely need fine-tuning — fo…

Mar 27, 202510 min read

Deep Dive

#ai#llm#fine-tuning

AI/MLAdvanced

Building AI Agents with Tool Use: From Chatbot to Autonomous Agent

A chatbot answers questions. An agent takes actions. The difference is tool use: the ability to call functions, search databases, execute code, and interact with external systems. When a model can look up real informatio…

Mar 23, 202510 min read

Deep Dive

#ai#agents#claude

AI/MLIntermediate

Prompt Engineering: Advanced Techniques for Production LLMs

Most prompt engineering tutorials stop at "be specific and provide context." That's necessary but not sufficient for production systems. This article covers the advanced techniques that separate demos from production-gra…

Feb 26, 202511 min read

Playbook

#ai#llm#prompt engineering

AI/MLAdvanced

Building a Production RAG System: Embeddings, Vector DBs, and Retrieval

Retrieval-Augmented Generation (RAG) is the most practical technique for making LLMs useful on your private data. Instead of hoping the model memorizes your documents during training (it doesn't), RAG retrieves relevant…

Feb 12, 202512 min read

Deep Dive

#ai#llm#rag