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Tutorials

In-depth AI tutorials — from fundamentals to production: RAG, agents, deployment, fine-tuning and more.

246

Total tutorials

44

Beginner

20

Hands-on

AdvancedOther

LLM Inference Optimization: vLLM, TensorRT-LLM, and Serving at Scale

LLM inference optimization: vLLM, TensorRT-LLM, and serving at scale (2026). KV cache is the bottleneck—PagedAttention + continuous batching are the biggest throughput levers. Other techniques include vLLM vs TensorRT-LLM selection, quantization, speculative decoding, prefix caching, and choosing smaller models.

LLM-inferencevLLM
11 min
AdvancedOther

Fine-Tuning LLMs with LoRA and QLoRA: Complete Guide 2026

Complete guide to fine-tuning large language models using LoRA and QLoRA techniques in 2026. Covers dataset preparation, training configuration, hardware requirements, evaluation metrics, and deploying fine-tuned models to production.

fine-tuninglora
50 min
AdvancedOther

ML Metadata Management

ML Metadata Management Overview Tracking ML artifacts, lineage, and provenance with MLMD. This guide covers practical implementation for production ML systems. Why This Matters in MLOps Modern ML systems require rigorous operations practices: - *

mlopsproduction
18 min
AdvancedOther

Multi-Vector RAG: Implementation Guide with Weaviate 2026

Multi-Vector RAG: Complete Implementation 2026 Overview Multi-Vector RAG is a specialized retrieval pattern that focuses on storing multiple embedding types per document. This guide shows you how to build a production-ready system using Weaviate.

ragmulti-vector
30 min
AdvancedOther

OpenAI Assistants API in Production: Building Reliable AI Features for SaaS Applications

Production guide for OpenAI Assistants API — thread lifecycle management, function calling, file search, code interpreter integration, streaming responses, and cost optimization strategies for SaaS products.

openaiassistants-api
15 min
AdvancedOther

Parent Document RAG: Implementation Guide with Chroma 2026

Parent Document RAG: Complete Implementation 2026 Overview Parent Document RAG is a specialized retrieval pattern that focuses on retrieving small chunks with large parent context. This guide shows you how to build a production-ready system using C

ragparent-document
30 min
AdvancedOther

Python AI Development Stack 2026: FastAPI + LangChain + Supabase

Complete guide to building production AI applications with FastAPI, LangChain, and Supabase in 2026. Covers project setup, async AI endpoints, RAG pipeline, vector search, and deployment.

fastapilangchain
20 min
AdvancedOther

PyTorch Lightning for Production Training: Best Practices and Advanced Features

Master PyTorch Lightning for production deep learning including multi-GPU training, mixed precision, gradient accumulation, callbacks, and integration with experiment tracking tools.

PyTorch-Lightningdeep-learning
30 min
AdvancedOther

RLHF vs DPO: Training LLMs from Human Feedback - Technical Guide 2025

A guide to preference learning for alignment (2026): turning a base model into a helpful, harmless, honest assistant. RLHF (SFT + reward model + PPO) is complex but powerful; DPO uses a single preference loss, skipping the reward model and RL, making it simpler and more stable. Includes a comparison table and variants like IPO/KTO.

RLHFDPO
11 min
AdvancedOther

Self-Query RAG: Implementation Guide with Qdrant 2026

Self-Query RAG: Complete Implementation 2026 Overview Self-Query RAG is a specialized retrieval pattern that focuses on AI-generated metadata filters for precise retrieval. This guide shows you how to build a production-ready system using Qdrant.

ragself-query
30 min
AdvancedOther

Stable Diffusion + ComfyUI for E-commerce Product Photography

Complete tutorial for e-commerce sellers using ComfyUI workflows with Stable Diffusion to create professional product photography, covering background replacement, lifestyle scene generation, and batch processing.

stable-diffusioncomfyui
15 min
AdvancedOther

Vector Databases & RAG in Production: Pinecone, Weaviate & pgvector in 2025

Retrieval-Augmented Generation (RAG) is the dominant pattern for grounding LLMs with up-to-date knowledge. This guide covers vector database selection (Pinecone, Weaviate, Qdrant, pgvector), embedding model selection and optimization, chunking strategies for documents, hybrid search (vector + keyword), re-ranking, evaluating RAG quality, and deploying production RAG systems that stay accurate over time.

RAGVector Database
23 min
AdvancedOther

Windsurf vs Devin vs SWE-agent: Autonomous Coding AI 2026

Windsurf vs Devin vs SWE-agent autonomous coding comparison (2026): ranked by autonomy—Windsurf human-in-the-loop, Devin fully autonomous AI engineer, SWE-agent open-source research framework (SWE-bench). Includes selection guide and production reliability.

windsurfdevin
9 min
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