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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

System DesignAdvanced

System Design: Designing a Real-time Recommendation Engine (TikTok / Netflix)

System Design: Designing a Real-time Recommendation Engine A recommendation engine is the "secret sauce" behind platforms like TikTok, Netflix, and Amazon. Its goal is to predict what a user will like next based on their…

Apr 20, 20263 min read

Deep Dive

#system-design#recommendation-engine#ai

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

AI Infrastructure on AWS: SageMaker, EKS GPU Scheduling, and Cost-Efficient Inference

Running AI workloads in production is a different discipline from running web services. The hardware is expensive, the failure modes are opaque, and the cost curve compounds fast. After operating inference infrastructure…

Apr 8, 202615 min read

Deep Dive

#aws#sagemaker#gpu

AI/MLAdvanced

LLM Evaluation at Scale: LLM-as-Judge, RAGAS, and Building Automated Eval Pipelines

Evaluating LLMs is the unsolved problem at the center of every production AI system. You can't ship a prompt change, a retrieval improvement, or a model upgrade without knowing whether it actually got better — or just be…

Apr 8, 202611 min read

Deep Dive

#llm evaluation#ragas#llm-as-judge

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/MLAdvanced

Vector Embeddings: The Foundation of Modern AI Applications

Every modern AI application — semantic search, RAG, recommendations, duplicate detection — is built on vector embeddings. An embedding converts text, images, or audio into a point in high-dimensional space where semantic…

Mar 11, 202511 min read

Deep Dive

#ai#embeddings#vector database

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