Openai

Learn to build a production-style RAG pipeline that demonstrates end-to-end systems engineering, from data ingestion to API serving.

Seven days of hands-on lessons for working engineers adopting OpenAI Codex, from repo-aware setup and prompt hygiene to review patterns and cost control.

Introduction In the last few years, the term agentic AI has moved from academic footnote to a central pillar of the industry’s roadmap. While “agentic” simply describes systems that can act autonomously toward a goal—selecting tools, planning, and iterating on their own—its practical realization has sparked a wave of new products, research directions, and engineering challenges. Few figures have shaped this shift as visibly as Sam Altman, CEO of OpenAI, whose public pronouncements, internal memos, and product launches have provided a de‑facto playbook for building and deploying agentic systems at scale. ...

Table of Contents Introduction Prerequisites & Environment Setup Understanding LangChain’s Agent Architecture OpenAI Function Calling: Concepts & Benefits Defining the Business Functions Building the Autonomous Loop State Management & Memory Real‑World Example: Automated Customer Support Bot Testing, Debugging, and Observability Performance, Cost, and Safety Considerations Conclusion Resources Introduction Autonomous agents are rapidly becoming the backbone of next‑generation AI applications. From dynamic data extraction pipelines to intelligent virtual assistants, the ability for a system to reason, plan, act, and iterate without human intervention unlocks powerful new workflows. In the OpenAI ecosystem, function calling (sometimes called “tool use”) allows language models to invoke external code in a structured, type‑safe way. Coupled with LangChain, a modular framework that abstracts prompts, memory, and tool integration, developers can build loops where the model repeatedly decides which function to call, processes the result, and decides the next step—effectively creating a self‑directed agent. ...

Introduction OpenAI’s Structured Outputs fundamentally change how developers build reliable applications on top of large language models. Instead of coaxing models with elaborate prompts to “return valid JSON,” you can now guarantee that responses conform to a precise JSON Schema or typed model, drastically reducing parsing errors, retries, and brittle post-processing.[1][2][7] This article explains very detailed structured outputs with OpenAI: what they are, how they differ from older patterns (like plain JSON mode), how to design robust schemas, integration patterns (Node, Python, Azure OpenAI, LangChain, third‑party helpers), and where to find the most useful documentation and learning resources. ...