Read practical AI engineering tutorials on RAG architecture, LLMOps pipelines, fine-tuning, and multi-agent workflows built for scalable applications.
RAG pulls external data at query time. Fine-tuning bakes knowledge into model weights. This guide breaks down when each approach works, what they cost, and how to pick the right one for your LLM application.
Read articleA step-by-step tutorial for building a production RAG pipeline in Python using LangChain and Pinecone. Covers document ingestion, chunking, embedding, retrieval, and answer generation with working code.
Read articleA hands-on guide to LLM fine-tuning with LoRA and QLoRA. Covers how both techniques work, when to use each, hardware requirements, and a working training example using Hugging Face PEFT.
Read articleBuild a working AI agent in Python using LangChain and OpenAI's function calling API. This tutorial covers tool creation, agent loops, memory, error handling, and deployment tips with runnable code.
Read articleA direct comparison of AutoGen, CrewAI, and LangGraph for building multi-agent AI systems. Covers architecture, ease of use, production readiness, and which framework fits which use case.
Read articleA practical comparison of Pinecone, Weaviate, and Chroma for AI applications. Covers pricing, performance, ease of use, managed vs self-hosted options, and which vector database fits your project size.
Read articleLLMOps is the set of practices for deploying, monitoring, and maintaining large language models in production. This guide covers the LLMOps stack, tooling, and the operational problems most teams hit after launch.
Read articlePractical techniques to cut LLM API costs by up to 80%. Covers prompt compression, caching, model routing, batching, and cheaper model substitution with real cost comparisons.
Read articleHow to deploy Llama 3 on AWS for production inference. Covers instance selection, vLLM setup, autoscaling, load balancing, and cost comparison with API-based models.
Read articlePrompt engineering techniques that hold up in production, not just demos. Covers system prompts, few-shot design, output formatting, chain-of-thought, and common mistakes that waste tokens and degrade quality.
Read articleHow to evaluate LLM outputs for accuracy, hallucination, and quality. Covers popular benchmarks, practical metrics, LLM-as-judge approaches, and testing frameworks you can run today.
Read articleArchitecture patterns for building multi-agent AI systems that work in production. Covers supervisor, swarm, and pipeline patterns with tradeoffs, failure modes, and practical design decisions.
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