Tutorials
In-depth AI tutorials — from fundamentals to production: RAG, agents, deployment, fine-tuning and more.
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Advanced RAG: Complete Guide 2026 – Beyond Basic Retrieval to Build Production-Grade Knowledge Bases
Basic RAG systems are easy to set up, but making them stable and effective in production is hard. This article dives deep into advanced RAG techniques: hybrid retrieval, reranking, multi-query decomposition, query routing, and systematic evaluation to improve RAG performance.
AI Agent Frameworks: LangChain, AutoGen & CrewAI for Production in 2025
AI agents go beyond chatbots—they use tools, maintain memory, plan multi-step tasks, and collaborate with other agents. This guide compares LangChain, LangGraph, AutoGen, and CrewAI for different use cases, covers reliable agent design patterns, tool calling best practices, memory architectures (short-term, long-term, episodic), handling errors and hallucinations, and deploying production agents with observability.
AI Agents in Production: Architecture Patterns and Reliability Engineering
AI agents—autonomous systems that use tools and make decisions to complete multi-step tasks—are moving into production at enterprise scale. This guide covers reliable agent architecture: tool design and error handling, state management for long-running agents, human-in-the-loop patterns, observability and debugging agents, graceful failure modes, security considerations, and testing strategies for non-deterministic systems.
AI Anomaly Detection for Time Series: From Statistical to Deep Learning Approaches
Build production anomaly detection systems for time series data using statistical methods, isolation forest, LSTM autoencoders, and modern time series foundation models for infrastructure and IoT monitoring.
AI Audio Production and Sound Design: Tools for Modern Sound Designers
How sound designers and audio producers use AI for sound synthesis, texture generation, spatial audio, game audio, and post-production workflows—with tool comparisons and practical techniques.
AI Canary Analysis
AI Canary Analysis: Safe Model Rollouts (2026): Route a new version to a small slice of traffic, compare against thresholds on operational + quality + safety metrics, and auto-promote or roll back. Includes mechanisms, Argo Rollouts/Flagger, per-region canaries, and fallback chains — putting automatic gates on fuzzy "better."
Deploying AI Computer Vision in Production: From Training to Edge
A practical guide to building and deploying computer vision systems at production scale—covering object detection, image classification, video analytics, and edge deployment strategies.
AI Drug Discovery in 2025: How Pharmaceutical Companies Cut Development Time from 12 to 4 Years
Technical overview of AI applications in pharmaceutical drug discovery — protein structure prediction, molecular generation, clinical trial optimization, and real examples from companies that accelerated drug development with AI.
ML Feature Store Architecture: Ensuring Consistency Between Online Serving and Offline Training Data
ML Feature Store Architecture (2026): Tackling training-serving skew—three sources of skew, offline/online dual storage with materialization synchronization, point-in-time join to eliminate time leakage. When you really need it (after being bitten), the convergence with vector stores in the LLM era, and practical tips for getting started with Feast.
AI Financial Report Analysis 2026: Quickly Decode Listed Company Reports with ChatGPT + Claude
Reading and analyzing financial reports is fundamental to investment decisions, but a full annual report often exceeds 200 pages. In 2026, AI can extract key information, analyze data, compare with competitors, and even identify anomalies and risk signals within 5-10 minutes. This article shares a complete AI workflow for financial report analysis, including financial health assessment, earnings quality analysis, management discussion interpretation, and risk factor identification, with a comprehensive prompt system.
AI Legal Research in 2025: How Attorneys Use Westlaw AI and LexisNexis+ AI to Win Cases
Guide to AI legal research tools including Westlaw AI and LexisNexis+ AI — case research acceleration, brief drafting assistance, contract review AI, and the ethical considerations for attorney use of AI.
AI Model Compression: Pruning, Quantization, and Knowledge Distillation
Three pillars of model compression: quantization (GPTQ/AWQ/GGUF, 8/4-bit), pruning, and knowledge distillation — principles, real tools, and accuracy/speed/memory trade-offs.
AI Model Merging: SLERP, TIES, DARE, and Model Soup Techniques
Model merging fuses multiple fine-tuned models into one without retraining. Based on mergekit: linear/SLERP/TIES/DARE/task-arithmetic methods, real YAML configs, and when to use each.
AI Model Quantization (GPTQ, AWQ): Complete Developer Guide 2026
A Complete Guide to AI Model Quantization (GPTQ/AWQ) (2026): Store weights with fewer bits to save VRAM and boost speed. Comparison of GPTQ vs AWQ, bitsandbytes/GGUF, selecting the 4-bit sweet spot, and a practical path of "directly downloading pre-quantized weights + deploying with vLLM/Ollama."
AI Predictive Maintenance in Manufacturing: Prevent Equipment Failures Before They Happen
How manufacturers deploy AI predictive maintenance to reduce unplanned downtime by 50%, extend equipment life, and optimize maintenance costs with sensor data and machine learning.
AI Process Optimization in Chemical and Process Manufacturing
How chemical, petrochemical, and process manufacturers use AI for real-time process optimization, yield improvement, energy reduction, and advanced process control.
AI Quality Control with Computer Vision: Automated Defect Detection for Manufacturers
Deploy AI computer vision for automated defect detection in manufacturing—covering hardware selection, model training with limited defect data, integration with production lines, and ROI analysis.
AI Quantitative Trading for Beginners 2026: Build Your Own Trading Strategy Backtesting System with Python + AI
Quantitative trading was once exclusive to institutional investors, but the rise of AI coding tools now allows individual investors to build their own quantitative strategies. This article guides you through building a complete quantitative trading backtesting system using Python + AI (ChatGPT/Cursor), covering data acquisition, strategy design, backtesting analysis, and risk control, with full code examples. Suitable for investment enthusiasts with basic Python skills.
Foundation Models for Robotics: RT-2, OpenVLA, and Physical Intelligence
Robotics foundation models and VLA (vision-language-action): core concepts, representative open research (RT-2, Open X-Embodiment, π0), and data/generalization challenges.
AI Red Teaming: Systematic Techniques for Finding LLM Vulnerabilities
Learn systematic red teaming techniques for identifying vulnerabilities in LLM systems including jailbreak methods, prompt injection attacks, multi-turn manipulation, and building comprehensive safety test suites.
AI Supply Chain Optimization for Manufacturers: From Procurement to Delivery
How manufacturers use AI to optimize supply chains end-to-end—covering supplier selection, procurement AI, production scheduling, logistics optimization, and supply chain risk management.
Advanced Time Series Forecasting with AI: N-BEATS, PatchTST, and Foundation Models
Advanced time-series forecasting: from classical (ARIMA/Prophet) to deep (N-BEATS/N-HiTS/TFT) to time-series foundation models (TimeGPT/Chronos/Moirai) — model selection, metrics, and backtesting.
Building Financial Analysis Agent with AI Agents: Complete Guide 2026
Building Financial Analysis Agent with AI Agents 2026 Introduction AI agents that can analyze financial data and generate investment reports are transforming how developers work. This guide shows you how to build a production-ready Financial Analys
Building SQL Query Agent with AI Agents: Complete Guide 2026
Building SQL Query Agent with AI Agents 2026 Introduction AI agents that can translate natural language to SQL and execute queries are transforming how developers work. This guide shows you how to build a production-ready SQL Query Agent using Lang
Claude Thinking vs OpenAI o3 vs Gemini 2.5 Pro: Reasoning AI 2026
Claude Extended Thinking vs OpenAI o3 vs Gemini Reasoning Mode (2026): All three trade longer thinking for accuracy—o3 excels at math/logic, Claude is strong in coding with transparent steps, and Gemini wins on long context and multimodality. Includes cost-saving strategies for difficulty-based routing.
Production Computer Vision with YOLO v11: Object Detection at Scale
Build production computer vision systems using YOLO v11 for object detection, including custom training, model optimization with TensorRT, edge deployment, and real-time video stream processing.
Contextual Compression RAG: Implementation Guide with Pinecone 2026
Contextual Compression RAG: Complete Implementation 2026 Overview Contextual Compression RAG is a specialized retrieval pattern that focuses on compressing retrieved context to fit LLM window. This guide shows you how to build a production-ready sy
Deploy Any GGUF Model on Ollama Local Server — Local development AI
Deploy Any GGUF Model on Ollama Local Server Overview Run Any GGUF Model directly on Ollama Local Server for local development AI. Local inference offers privacy, zero latency, and no ongoing API costs. **Specs**: CPU/GPU auto · Variable Installa
Deploy Any ONNX Model on ONNX Runtime CrossPlatform — Cross-platform deployment
Deploy Any ONNX Model on ONNX Runtime CrossPlatform Overview Run Any ONNX Model directly on ONNX Runtime CrossPlatform for cross-platform deployment. Local inference offers privacy, zero latency, and no ongoing API costs. **Specs**: ONNX Runtime ·
Deploy GGUF Models on LM Studio Desktop — No-code local AI GUI
Deploy GGUF Models on LM Studio Desktop Overview Run GGUF Models directly on LM Studio Desktop for no-code local AI GUI. Local inference offers privacy, zero latency, and no ongoing API costs. **Specs**: CPU/GPU · 8GB+ Installation ```bash Insta
Deploy Llama 3.1 70B on vLLM Production Serving — High-throughput serving
Deploy Llama 3.1 70B on vLLM Production Serving Overview Run Llama 3.1 70B directly on vLLM Production Serving for high-throughput serving. Local inference offers privacy, zero latency, and no ongoing API costs. **Specs**: NVIDIA A100 · 80GB VRAM
Deploy Mistral 7B on Intel Core Ultra Laptop — Laptop inference
Deploy Mistral 7B on Intel Core Ultra Laptop Overview Run Mistral 7B directly on Intel Core Ultra Laptop for laptop inference. Local inference offers privacy, zero latency, and no ongoing API costs. **Specs**: Intel NPU · 16-32GB Installation ``
Deploy TinyLlama 1.1B on Raspberry Pi 5 — Home automation assistant
Deploy TinyLlama 1.1B on Raspberry Pi 5 Overview Run TinyLlama 1.1B directly on Raspberry Pi 5 for home automation assistant. Local inference offers privacy, zero latency, and no ongoing API costs. **Specs**: ARM CPU · 4GB RAM Installation ```ba
Diffusion Models Explained: From DDPM to Stable Diffusion and FLUX
A detailed technical walkthrough of diffusion models (2026): from DDPM forward/reverse process math, UNet noise prediction, to Latent Diffusion (Stable Diffusion's 64x efficiency trick), CFG guidance formula, and DiT with rectified flow (SD3/FLUX). Includes a theory-to-tool comparison table.
Fine-Tuning GPT-4o Mini: OpenAI Fine-Tuning API Complete Guide
GPT-4o Mini Fine-Tuning Complete Guide (2026): Use OpenAI's fine-tuning API to obtain a hosted model with stable format/style and reduce costs for massive calls. Includes real code for JSONL data preparation → upload → training → inference, when to fine-tune vs. prompt/RAG, and data quality > quantity.
Build a Full-Stack AI SaaS App with Next.js 16, Clerk, and Supabase 2026
Complete tutorial for building a full-stack AI SaaS application using Next.js 16, Clerk for authentication, Supabase for database, and OpenAI for AI features. Covers user management, usage metering, stripe billing, and deploying to production.
Graph RAG: Implementation Guide with Neo4j 2026
Graph RAG: Complete Implementation 2026 Overview Graph RAG is a specialized retrieval pattern that focuses on knowledge graph traversal for multi-hop reasoning. This guide shows you how to build a production-ready system using Neo4j. Why Graph RAG
Hugging Face SFT Trainer: Hands-On Tutorial
Hugging Face SFT Trainer Overview Supervised fine-tuning with Hugging Face TRL SFTTrainer. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl acce
Hybrid Search RAG: Implementation Guide with Elasticsearch 2026
Hybrid Search RAG: Complete Implementation 2026 Overview Hybrid Search RAG is a specialized retrieval pattern that focuses on combining vector and keyword search for maximum recall. This guide shows you how to build a production-ready system using
LangChain LCEL: Advanced Patterns for Production AI Applications
LangChain Expression Language (LCEL) is the modern way to build composable LLM pipelines. This guide covers advanced LCEL patterns: parallel execution, streaming, dynamic routing, conditional chains, retry and fallback logic, tool use orchestration, and testing strategies. Includes production patterns for RAG applications, multi-step agents, and complex data transformation pipelines with real performance benchmarks.
LangChain vs LangGraph 2026 Deep Dive: When to Use Which Framework?
LangChain vs LangGraph comparison (2026): Chains handle linear flows, Graphs handle stateful Agents (loops/approval/persistence)—one rule of thumb: does the runtime need to change paths based on results? Includes decision table, combined usage (graph orchestration + chain for single steps), and AgentExecutor migration path.
LangChain vs LlamaIndex vs Haystack: RAG Framework 2026
Detailed comparison of LangChain, LlamaIndex, and Haystack for building RAG pipelines. Covers document processing, retrieval strategies, performance benchmarks, and production deployment for 2026.
LangGraph Complete Guide 2026: Build Stateful AI Agents
LangGraph Complete Guide (2026): Build agents as state machines—State+reducer, conditional edges, checkpointer persistence (multi-turn memory/crash recovery/time travel), interrupt for human approval gates, multi-agent supervisor pattern. Includes an honest comparison of when to use it and when not to.
LlamaIndex Tutorial 2026: Build Production RAG Applications
Complete LlamaIndex tutorial 2026. Covers VectorStoreIndex, persistent Qdrant storage, chat engines, sub-question decomposition, semantic chunking, metadata filtering, and streaming.
LLM Application Architecture Patterns: From Simple to Complex Systems
LLM application architecture patterns: RAG, agents/tool calling, model routing, caching, evals, guardrails, and observability — real patterns and trade-offs.
LLM Fine-Tuning for Production: LoRA, QLoRA & RLHF in 2025
Fine-tuning LLMs allows adapting powerful foundation models to specific domains without training from scratch. This guide covers LoRA and QLoRA for parameter-efficient fine-tuning, dataset preparation and quality filtering, instruction tuning format, RLHF and DPO for alignment, fine-tuning on consumer GPUs with quantization, evaluation with domain benchmarks, and deploying fine-tuned models with vLLM or TGI for production serving.
LLM Fine-tuning with LoRA: Complete Developer Guide 2026
A complete guide to fine-tuning large models with LoRA (2026): freeze the base model, train only low-rank adapters, finish in hours on a single GPU; QLoRA trains adapters on a 4-bit base. Includes real PEFT code, when to fine-tune (vs. prompting/RAG), and practical tips that data quality > quantity.
Reducing LLM Hallucinations: Practical Techniques for Production Applications
LLM hallucination—generating confident but false information—is the primary reliability challenge in production AI applications. This guide covers the root causes of hallucination, detection strategies (fact-checking layers, self-consistency checks, confidence calibration), mitigation techniques (RAG, constrained generation, chain-of-thought verification), and monitoring approaches for production systems. Includes benchmark data on hallucination rates across different model and technique combinations.