Anthropic System Design Interview Preparation Guide: Architecture & Complete Framework
The only interview guide you need to master the 6-layer stack, Constitutional AI, hardware routing, safety tradeoffs, and 6 real interview questions with model answers
And why your non computer science PhD is your competitive advantage; A golden chance to Science (Physics, Chemistry, Biology, Maths, Medical) Ph.Ds or even any Ph.D to elevate their career.
Why Your Perfect Prompt Fails: Survival Strategies for Production-Grade AI
A guide to scaling LLM applications by mastering context engineering, observability, and output guardrails to ensure production stability under variance. Also get 6 interview questions around it at the end of article.
How to Implement Prompt Caching: Reduce AI API Costs by 90% with Real Examples
A definitive technical guide for AI Product Managers and leaders, featuring advanced cache patterns, ROI frameworks, P95 metrics, and implementation examples for Claude.
How to Reduce AI Agent Latency: Proven Architecture Patterns & Metrics to Make Gen AI Faster
Learn proven architecture patterns to reduce AI agent latency and metrics such as TTFT, P95 Latency, E2E Latency. Optimize the prompts, parallel workflows, and caching for faster AI responses.
AI Prompt Engineering for Product Managers: From Basics to Advanced
Advanced AI Prompts Every PM Needs to Master in 2026 - Learn how to craft, evaluate, and optimize prompts to control AI behavior, maximize usefulness, and ensure alignment with product and business goals.
From Metrics to Insights: Building Reliable AI Eval Processes
Explore how PMs can manage stochastic outputs, collect high-quality labels, and integrate offline and online evaluations to ensure AI delivers value and trust.
The AI PM’s Guide to Evaluation Metrics: Measuring Model Performance and Business Impact
From technical benchmarks to business outcomes: A strategic framework for selecting, tracking, and translating AI model performance into product success
Measuring RAG Performance: A Definitive Guide to Metrics, Tools, and Best Practices
Evaluating a Retrieval-Augmented Generation (RAG) system goes beyond just generating answers. In this article, we break down all essential RAG evaluation metrics—from nDCG, BLEU, and ROUGE to precision, recall, coverage, and human evaluation. With simple examples and practical explanations, you’ll learn how to measure relevance, accuracy, efficiency, consistency, and user satisfaction in your RAG pipelines, ensuring your AI delivers reliable, high-quality answers every time.
