MirahLabs Engineering Blog
Technical insights, architectural deep-dives, and system designs authored by our product engineers and AI research leads.
Understanding Transformer Architecture: Attention Is All You Need
A deep-dive into the Transformer model that revolutionized natural language processing—from self-attention heads to positional encoding and multi-head parallelism.
AI Agent Evaluation Frameworks: Ragas, TruLens, and Phoenix
Evaluating LLM outputs is notoriously difficult. Learn how to use automated evaluation frameworks to measure RAG faithfulness, answer relevance, and context precision.
Feature Engineering for Machine Learning: From Raw Data to Model-Ready Features
Feature engineering is the most impactful step in the ML pipeline. Learn how to handle missing data, encode categoricals, create interaction features, and use automated feature selection.
AI Agents and Tool Use: Building Autonomous Workflows with LangGraph
LangGraph enables stateful, multi-step AI agent workflows with cyclic graphs. Learn how to build reliable autonomous agents that use tools, handle errors, and maintain state.
Fine-Tuning LLMs with LoRA: A Practical Guide
Low-Rank Adaptation (LoRA) lets you fine-tune large language models efficiently with minimal GPU memory. Learn how to apply LoRA to domain-specific AI tasks step by step.
Diffusion Models Explained: DALL-E 3 and Stable Diffusion Mechanics
Understand the math and mechanics behind modern generative image models: forward diffusion, reverse denoising U-Nets, and classifier-free guidance.