Llm

Python LLM applications often start small: a FastAPI route, a call to an LLM provider, some prompt engineering, and you’re done. Then traffic grows, latencies spike, and your CPUs sit mostly idle while users wait seconds—or tens of seconds—for responses. What went wrong? One of the most common and least understood culprits is thread pool starvation. This article explains what thread pool starvation is, why it’s especially dangerous in Python LLM apps, how to detect it, and concrete patterns to avoid or fix it. ...

Context engineering is the systematic discipline of selecting, structuring, and delivering optimal context to large language models (LLMs) to maximize reliability, accuracy, and performance—far beyond basic prompt engineering.[1][2] This zero-to-hero tutorial equips developers with foundational concepts, advanced strategies, practical Python implementations using Hugging Face Transformers and LangChain, best practices, pitfalls, and curated resources to build production-ready LLM systems.[1][7] What is Context Engineering? Context engineering treats the LLM’s context window—its limited “working memory” (typically 4K–128K+ tokens)—as a critical resource to be architected like a database or API pipeline.[2][5] It involves curating prompts, retrievals, memory, tools, and history to ensure the model receives the right information at the right time, enabling plausible task completion without hallucinations or drift.[1][4][6] ...

Large Language Models (LLMs) excel at processing text, but they lack real-time web access. By integrating browsers, developers can empower LLMs to fetch live data, automate tasks, and interact dynamically with websites. This zero-to-hero tutorial covers core methods—browser APIs, web scraping, automation, and agent pipelines—with practical Python/JS examples using tools like LangChain, Playwright, Selenium, and more. Why Browsers + LLMs? Key Use Cases Browsers bridge LLMs’ knowledge gaps by enabling: ...

The OpenAI Cookbook is an official, open-source repository of examples and guides for building real-world applications with the OpenAI API.[1][2] It provides production-ready code snippets, advanced techniques, and step-by-step walkthroughs covering everything from basic API calls to complex agent workflows, making it the ultimate resource for developers transitioning from LLM theory to practical deployment.[4] Whether you’re new to OpenAI or scaling AI features in production, this tutorial takes you from setup to mastery with the Cookbook’s most valuable examples. ...

Retrieval-Augmented Generation (RAG) revolutionized AI by grounding LLMs in external knowledge, but traditional flat-chunk retrieval struggles with long, complex documents requiring multi-hop reasoning. RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) solves this by building hierarchical trees of clustered summaries, enabling retrieval across abstraction levels for superior context and accuracy.[1][2] In this zero-to-hero tutorial, you’ll learn RAPTOR’s mechanics, why it outperforms standard RAG, and how to implement it step-by-step with code. We’ll cover pitfalls, tuning, and best practices, empowering developers to deploy production-ready pipelines. ...