21 articles
Build a production Retrieval-Augmented Generation app from scratch using LangChain, OpenAI embeddings, and ChromaDB. Covers chunking, reranking, evaluation, and FastAPI deployment.
Master the OpenAI API in 2026: GPT-4o chat completions, vision, function calling, embeddings, structured outputs, streaming, and cost optimization. Complete Python examples for every major feature.
Build a production AI chatbot with Next.js 15 App Router, OpenAI GPT-4o, and the Vercel AI SDK. Streaming responses, chat history, rate limiting, and Vercel deployment. Full TypeScript code included.
Build a production semantic search engine using OpenAI embeddings and cosine similarity in Python. Covers hybrid search, caching, FastAPI deployment, and cost optimization for high-volume use cases.
Build an AI code review system using GPT-4o: automated bug detection, security scanning, GitHub Actions PR comments, pre-commit hooks, and custom rule sets. Complete Python implementation included.
A practical guide to building rigorous LLM evaluation pipelines in 2026 using RAGAS, LLM-as-judge, automated benchmarks, and production monitoring. Designed for AI engineers who need to prove quality, catch regressions, and compare models confidently.
How developers can use ChatGPT for code generation, debugging, architecture review, and API integration. Covers GPT-4o capabilities, proven prompt patterns, and cost optimization.
Complete technical guide to GPT-4o covering its multimodal capabilities, API usage, context window, pricing, and how it compares to GPT-4-turbo. Built for developers evaluating or integrating OpenAI models.
Complete guide to the OpenAI Assistants API for building stateful AI agents with persistent threads, file search, code interpreter, and function calling. Includes Python examples from setup to production.
Step-by-step tutorial on OpenAI function calling (tool use) with Python examples. Covers tool definition, parallel calls, multi-step agent loops, and structured data extraction.
ChatGPT Tools, GPT Actions, and custom GPTs let developers build specialized AI workflows for code review, data analysis, documentation generation, and more. This guide covers the most useful developer configurations and how to build your own.
Master the OpenAI Embeddings API — text-embedding-3-small vs large, batch embedding, dimensionality reduction, cost optimization, and full RAG integration. Everything you need to build production semantic search.
Master LLM fine-tuning — when to fine-tune vs prompt engineer, OpenAI fine-tuning API, open-source fine-tuning with Hugging Face, data preparation, and evaluation. A practical guide for production AI teams.
A comprehensive guide to advanced prompt engineering techniques that maximize LLM output quality, covering zero-shot, few-shot, chain-of-thought, and structured prompting patterns. Ideal for developers and AI practitioners building production LLM applications.
Learn how to use few-shot learning to teach LLMs new tasks with minimal examples, covering example selection, formatting, domain adaptation, and evaluation strategies. Targeted at AI engineers building classification, extraction, and generation pipelines.
A practical guide to writing system prompts that reliably shape LLM behavior, covering structure, role assignment, output format control, guardrails, and testing strategies. Designed for developers building production AI applications.
A comprehensive guide to building autonomous LLM agents with tool use, covering the ReAct pattern, function calling, memory systems, and multi-agent coordination — with production-ready Python examples. For ML engineers and backend developers building agentic AI systems.
Learn how to build LLM-powered applications with LangChain and Python. This guide covers chains, prompts, retrieval-augmented generation (RAG), agents, and real-world AI app patterns for 2026.
Reduce AI costs by 50% with OpenAI Batch API. Process embeddings, classifications, and reports offline with intelligent pipeline design.
Learn when and how to fine-tune OpenAI models in production, including dataset preparation, cost optimization, and evaluation strategies.
Explore OpenAI''s Responses API for managing conversation state, tools, and long-lived interactions without manual history management.