MirahLabs Engineering Blog
Technical insights, architectural deep-dives, and system designs authored by our product engineers and AI research leads.
Building Production RAG Pipelines with LangChain and PostgreSQL pgvector
How to build Retrieval-Augmented Generation (RAG) systems that ground LLM responses in private documents using LangChain orchestration and PostgreSQL's pgvector extension.
Building Multi-Agent AI Systems with CrewAI
Multi-agent AI systems assign specialized roles to different LLM agents that collaborate to complete complex tasks. Learn how CrewAI orchestrates agent crews for research, writing, code review, and more.
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.
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.
Quantization Techniques for LLMs: FP16 to INT4 and GPTQ
Deploying LLMs on local hardware requires massive memory footprint reductions. Learn how post-training quantization techniques like GPTQ and GGUF compress models from FP16 to INT4.