Building a Multi-Head Self-Attention Block with RoPE and ALiBi from Scratch
A hands-on guide to building a multi-head self-attention block with RoPE and ALiBi from scratch, signaling advanced ML and systems engineering skills to hiring managers.
Ai-Architecture
Becoming a Claude Certified Architect: What It Actually Takes
Breaking down Anthropic’s Claude Certified Architect track — the domains it tests, the production patterns it expects you to know, and a practical study plan for engineers who already ship LLM systems.
RAG vs CAG: What the Difference Actually Means in Production
A practical, architecture-first comparison of Retrieval-Augmented Generation and Cache-Augmented Generation, with patterns for combining both in production LLM systems.
Navigating the Shift from Large Language Models to Agentic Reasoning Frameworks in 2026
Table of Contents Introduction Recap: The Era of Large Language Models 2.1. Strengths of LLMs 2.2. Limitations That Became Deal‑Breakers What Are Agentic Reasoning Frameworks? 3.1. Core Components Why the Shift Is Happening in 2026 4.1. Technological Drivers 4.2. Business Drivers Architectural Comparison: LLM Pipelines vs. Agentic Pipelines Building an Agentic System: A Practical Walkthrough 6.1. Setting Up the Environment 6.2. Example: A Personal Knowledge Assistant 6.3. Key Code Snippets Migration Strategies for Existing LLM Products Challenges and Open Research Questions Real‑World Deployments in 2026 9.1. Case Study: Customer‑Support Automation 9.2. Case Study: Autonomous Research Assistant Best Practices and Guidelines Future Outlook: Beyond Agentic Reasoning Conclusion Resources Introduction The last half‑decade has seen large language models (LLMs) dominate headlines, research conferences, and commercial products. From GPT‑4 to Claude‑3, these models have demonstrated remarkable fluency, few‑shot learning, and the ability to generate code, prose, and even art. Yet, as we entered 2026, a new paradigm—Agentic Reasoning Frameworks (ARFs)—has begun to eclipse pure‑LLM pipelines for many enterprise and research use‑cases. ...
Mastering Vector Databases for High Performance Retrieval Augmented Generation and Scalable AI Architectures
Table of Contents Introduction Why Vector Databases Matter for RAG Core Concepts of Vector Search 3.1 Embedding Spaces 3.2 Similarity Metrics Indexing Techniques for High‑Performance Retrieval 4.1 Inverted File (IVF) + Product Quantization (PQ) 4.2 Hierarchical Navigable Small World (HNSW) 4.3 Hybrid Approaches Choosing the Right Vector DB Engine 5.1 Open‑Source Options 5.2 Managed Cloud Services Integrating Vector Databases with Retrieval‑Augmented Generation 6.1 RAG Pipeline Overview 6.2 Practical Python Example (FAISS + LangChain) Scaling Strategies for Production‑Grade AI Architectures 7.1 Sharding & Replication 7.2 Batching & Asynchronous Retrieval 7.3 Caching Layers Performance Tuning & Monitoring 8.1 Metric‑Driven Index Optimization 8.2 Observability Stack Security, Governance, and Compliance Real‑World Case Studies Future Directions and Emerging Trends Conclusion Resources Introduction Retrieval‑Augmented Generation (RAG) has become the de‑facto paradigm for building knowledge‑aware language models. Instead of relying solely on a model’s internal parameters, RAG pipelines fetch relevant context from an external knowledge store and inject it into the generation step. The quality, latency, and scalability of that retrieval step hinge on a single, often underestimated component: the vector database. ...