Posted inAI
Beyond the ‘Vibe Check’: Quantifying RAG Performance with RAGAS
Stop relying on 'vibes' to test your AI. Learn how to use RAGAS to quantify Faithfulness, Relevancy, and Recall with automated LLM-based scoring.
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Fixing RAG Retrieval Failures: A Practical Guide to BM25 and Hybrid Search
Vector search often misses exact technical terms and specific IDs. This guide explains how to combine BM25 and Vector Search using Hybrid Search and RRF to increase RAG accuracy by up to 20%.
Posted inAI
LLM Evaluation: Practical Methods and Tools to Measure AI Model Quality in Production
LLM evaluation is the practice of systematically measuring whether your AI model's outputs are accurate, relevant, and safe — not just fast. This guide covers practical setup with DeepEval and RAGAS, CI/CD integration, and production monitoring strategies drawn from real deployment experience.
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Extract and Process PDF Tables with Docling for RAG Systems
After 6 months of using Docling in production for PDF-heavy RAG systems, I share how to extract tables properly, chunk them intelligently, and index them for accurate retrieval. Covers quick start, pipeline setup, and hard-won practical tips.
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Build a Text-to-SQL Tool with Vanna.ai: Talk to Your Data in Plain English
Tired of writing the same SQL queries? Learn how to build a production-ready Text-to-SQL system using Vanna.ai and Python to empower non-technical users.
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RAG Explained: How to Keep Your LLMs Honest in Production
Is your LLM hallucinating or providing outdated info in production? Retrieval-Augmented Generation (RAG) is your solution. This tutorial details RAG's architecture and offers a Python example for building reliable, fact-based AI systems.
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Vector Database Explained: Pinecone, Weaviate, ChromaDB for AI Engineers
Vector databases like Pinecone, Weaviate, and ChromaDB are crucial for today's AI applications and semantic search. This tutorial explains the core problem they address, compares their features, and offers practical code examples to guide your project's tool selection.
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AI Hallucination Explained: What It Is and How to Detect It in Your Apps
AI hallucination causes language models to generate confident but factually wrong answers — a real problem for any app built on LLMs. This guide covers what causes hallucinations, the common types you'll encounter, and practical Python techniques to detect and reduce them in your own projects.
Posted inAI
Perplexity AI Explained: The AI-Powered Search Engine for Smarter Research
Perplexity AI is an innovative AI-powered search engine that synthesizes information from the web into direct, cited answers. It uses Retrieval-Augmented Generation (RAG) to provide trustworthy results, enhancing research efficiency for IT professionals.