This guide walks you through three practical methods—using Ollama, LM Studio, and GPT4All—to install and run the open‑source Llama 3 model locally on Windows, macOS, or Ubuntu, including command‑line usage, Python integration, and prompt‑engineering techniques for formatted outputs.
This article explains how large language models work, why well‑crafted prompts are essential, and presents practical strategies—such as clarity, conciseness, focus, role‑setting, delimiters, few‑shot examples, and step‑by‑step instructions—to help users obtain accurate and relevant responses from AI systems.
The article explores how generative AI transforms business intelligence by detailing BI's evolution, the ChatBI platform's architecture, NL2SQL challenges, performance and accuracy optimizations, and real‑world deployment outcomes that demonstrate reduced user barriers and enhanced analytical efficiency.
This article investigates how large language models can be applied to recommendation tasks, describing two usage strategies, various ranking approaches, experimental evaluations on multiple datasets, comparisons with traditional models, and analyses of prompt design, cost, and cold‑start capabilities.
This article outlines ten practical principles for designing applications with large language models, emphasizing a model‑first mindset, precision through interactive disambiguation, clear division of code and model responsibilities, data quality, handling uncertainty, and recognizing the limits of LLMs to build robust, maintainable software.
This article provides an in‑depth overview of the Coze low‑code AI bot platform, covering its core features, product comparisons, step‑by‑step bot creation, RAG implementation, plugin usage, memory mechanisms, cron jobs, agent design, advanced workflow techniques, quality management, and future prospects.
The diagram contrasts a Base LLM, which merely predicts the next word from training data and can continue stories or answer simple facts but may generate unsafe text, with an Instruction‑Tuned LLM that is fine‑tuned via RLHF to understand and follow commands, delivering more accurate, useful, and safe responses.
The article explains that current large language models lack true self‑awareness and act as assistants, so achieving convincing role‑playing behavior requires dedicated system prompts, specialized data, careful balance of continue pre‑training and general SFT, and evaluation methods to detect dissonance and preserve base capabilities.
Senior algorithm engineer Qi Jianwei from Xiaomi presents a comprehensive overview of building a large‑model‑centric Agent framework for voice assistants, covering prompt design, information retrieval, RAG processes, and future optimization directions to enhance performance and stability.
This article explains how to construct effective SQL prompts by combining personal SQL expertise with large language model capabilities, introduces the open‑source sql‑translator tool for bidirectional SQL‑natural language conversion, and discusses practical productization scenarios.
This article explains how to create, configure, and enhance AI bots on the Coze platform using a three‑step process, advanced workflow design, prompt engineering, and plugin integration, providing practical tips, code examples, and best‑practice recommendations for reliable content extraction and summarization.
This comprehensive review examines Sora, the first model capable of generating minute‑long, high‑quality videos from text, covering its historical background, core diffusion‑Transformer architecture, data preprocessing strategies, prompt engineering techniques, diverse applications, and the ethical and technical limitations that shape its future.
This article explains how LangChain simplifies building and integrating large language model applications by providing modular components such as models, prompts, indexes, tools, memory, chains, and agents, illustrated with practical use cases like travel assistants, face‑recognition troubleshooting, and multi‑agent workflows.
OpenAI’s newly released Sora model, built on the DALL‑E 3 foundation, can generate up to 60‑second high‑quality videos from text prompts, offering features such as multi‑character scenes, seamless video synthesis, image‑to‑video animation, physical world simulation, and prompting guidance for designers, while raising ethical and creative challenges.
This article explains how to use ChatGPT to automatically generate accurate pandas scripts for Excel‑based data analysis, compares Python packages with VBA, shows prompt engineering techniques, and demonstrates adapting the code for Excel's built‑in Python feature.
This article demonstrates how to leverage ChatGPT to automatically extract structured key information from conversational text by defining input keywords, crafting effective prompts, testing iterative refinements, and integrating the process into a Coze workflow that outputs clean JSON data.
This guide explains how to use the Coze no‑code AI Bot platform to create a personal blog knowledge base by configuring plugins, data sources, persistence, workflow nodes, prompt engineering, and publishing the bot for seamless AI‑assisted content retrieval.
InstantID is a plug‑in adapter that adds identity‑preserving capabilities to text‑to‑image diffusion models, allowing users to upload a single photo and, within 30 seconds, produce a Spring Festival‑styled portrait with accurate facial features, customizable prompts, and strong text control.
This article walks through the ChatGPT interface for creating custom GPTs, explains configuring basic information, details the five built‑in tools—knowledge base, web browsing, DALL·E image generation, code interpreter, and external API actions—and demonstrates each with concrete prompts and screenshots.
Retrieval‑augmented generation (RAG) enhances large language models by fetching up‑to‑date, authoritative information from external sources, addressing hallucinations, outdated knowledge, and lack of citations, while offering cost‑effective implementation, improved relevance, user trust, and greater developer control through vector databases, semantic search, and prompt engineering.
This article details how MaShang Consumer Finance leverages large language models for sales, collection, and customer service, covering company background, AI research achievements, model training infrastructure, data‑quality and compliance challenges, prompt engineering, inference acceleration, evaluation methods, and lessons learned from real‑world deployment.
This article walks you through five progressive levels—from basic usage to prompt engineering, custom instructions, bespoke Prompt bots, and finally creating OpenAI GPTs—demonstrating how structured prompts, role setting, output formatting, and integrated extensions can dramatically boost ChatGPT’s effectiveness for professional tasks.
This guide explains how AI image generators such as Stable Diffusion and DALL·E 3 turn text prompts into pictures by using diffusion models, CLIP‑aligned embeddings, and optional controls like negative prompts, fine‑tuned LoRA checkpoints and ControlNet conditioning, highlighting their differences, workflow, and practical customization.
This article details Baidu Intelligent Cloud's database operations team’s end‑to‑end design of an AI‑driven knowledge‑base Q&A system, covering background, architecture, technical choices, module implementation, key challenges such as vector‑search recall and token limits, and real‑world deployment scenarios.
Sun Ke’s keynote at the 2023 Baidu Cloud Intelligence Conference explains how AI‑native development has shifted from model selection to building practical applications, and introduces Baidu Cloud AppBuilder—a three‑layer, low‑code‑and‑code platform that provides multimodal, LLM, and infrastructure services, enabling rapid prototyping of solutions such as automated resume screening and interview preparation.
This article outlines three practical approaches—direct prompting, retrieval‑augmented generation (RAG), and fine‑tuning—to connect enterprise applications to large language models, explains key prompt‑engineering techniques, details RAG workflow and vector‑database integration, and provides step‑by‑step guidance for fine‑tuning on the KubeAI platform.
This article explains how Prompt Engineering combined with the CAMEL framework enables role‑playing AI agents to automatically generate and manage prompts, illustrates the concept with a stock‑trading example, and provides Python code using LangChain to build a marketing‑automation agent for a small business.
This article explores the IP-Adapter technique for Stable Diffusion, comparing it with conventional img2img, detailing its superior prompt integration, multi‑reference capabilities, workflow automation with ControlNet and ComfyUI, and how it enables instant LoRA creation for faster, more diverse AI‑generated images.
This article explains Microsoft’s open‑source Semantic Kernel SDK, describing its purpose, core components, workflow (Ask, Planner, Skills, Memory, Connectors, Pipeline), practical examples, and the advantages it brings to developers building Copilot‑style AI applications.
This article presents the design, implementation, and measured impact of an AI‑driven development assistant integrated into the IDE, detailing its features such as intelligent context filling, custom Prompt templates, quick error diagnosis, internal system integration, and smart code completion, while also discussing limitations of existing tools like Copilot and ChatGPT and outlining future development plans.
This article explores how AI agents, particularly those built with LangChain, can enhance Text2SQL performance by decomposing queries, leveraging tools, memory, and planning, and provides practical code examples and future directions for developers.
The proposed unified platform centralizes prompt creation, evaluation, and iteration for large‑model applications, offering one‑stop hosting, metric‑driven testing, seamless resource integration, model switching, fine‑grained traffic control, and an automated data‑flywheel with QEP scoring, cutting optimization cycles from weeks to days while paving the way for advanced fine‑tuning techniques.
This guide shows how to use LangChain with OpenAI’s API, Puppeteer, and vector stores to automatically read local or web‑based API documentation, split and retrieve relevant text, and prompt an LLM to generate ready‑to‑use TypeScript front‑end code, highlighting setup, prompt design, and example outputs.
This guide walks you through using GPT to craft detailed prompts, refining them on a prompt‑tool platform, and training a custom Midjourney style model via Discord commands, covering data selection, parameter settings, and best‑practice tips.
A community-driven investigation reveals that GPT‑4’s output length drops in December, with statistical tests showing a significant reduction, sparking debates about seasonal effects, model prompts, temperature settings, and the need for further reproducible experiments.
This case study details how MEUX’s designers leveraged Stable Diffusion to mass‑produce high‑quality ad images for Baidu App, overcoming the scalability challenges of personalized advertising through prompt engineering, template design, and human‑AI collaboration.
This article examines the challenges of LLM‑based Text‑to‑SQL such as hallucinations, data‑security risks, and user input errors, and presents prompt‑engineering strategies, fine‑tuning comparisons, prompt types, code examples, and experimental results to improve reliability and cost‑effectiveness.
This article explains the concept of AI Agents in the large‑language‑model era, details LangChain's implementation mechanics, shares practical challenges and optimizations encountered by NetEase Cloud Music, and provides step‑by‑step code examples and performance insights for building robust AI Agents.
Alibaba Cloud’s AI platform PAI had four papers accepted at EMNLP 2023, presenting advances in automatic prompt engineering for text‑to‑image, domain‑specific knowledge‑enhanced language models, cognitive‑tree reasoning with small LLMs, and cross‑lingual machine reading comprehension, all demonstrating cutting‑edge AI research and product integration.
This article reviews the evolution from MLOps to LMOps, outlines the core concepts, challenges, and key technologies such as large‑model inference optimization, prompt engineering, and context‑length extension, and offers a forward‑looking perspective on the future of AI operations.
The article outlines a structured approach to identifying, categorizing, evaluating impact, and repairing undesirable responses from large language models, covering both model‑level interventions across training stages and practical inference‑time techniques such as parameter tuning, prompt engineering, RAG, and pre/post‑processing safeguards.
This article describes how an enterprise leveraged GPT‑3.5 and other large language models to create a private knowledge base, design prompt engineering, implement plugin extensions, and build a secure, scalable backend and front‑end integration platform that enables AI‑driven customer‑service assistants across multiple business lines.
This article details how 58.com’s design team leveraged AI tools such as Stable Diffusion, LoRA models, and prompt engineering to create a visually rich September recruitment landing page, outlining the project background, design strategy, technical exploration, and measurable results.
This article walks through the newly released GPTs feature in ChatGPT, showing how Plus users can create, configure, and test custom GPTs, integrate external APIs, and share their creations while highlighting current limitations and future potential.
During the Saturday session, we reviewed ChatGLM2’s upgrades—Multi‑Query Attention and FlashAttention—demonstrated deployment on Ascend + ModelArts + MindSpore, and introduced ChatGLM3’s revamped prompt design, native tool‑calling and code‑interpreter capabilities, while previewing the next lecture on text‑generation decoding.
This article reviews the evolution from MLOps to LMOps, outlines the fundamentals, challenges, and key technologies of large‑model operations—including inference optimization, prompt engineering, and context‑length extension—and presents Baidu AI Cloud's platform solutions and future outlook.
Fin2.0, NetEase Cloud Music’s AI‑driven design assistant, combines text‑to‑image, text‑to‑icon and text‑to‑copy generation with an internal Stable Diffusion engine and streamlined prompt templates, enabling non‑designers like a colleague to create high‑quality promotional banners in hours while avoiding external costs and data‑security risks.
In this interview, Baidu's search architect Tianbao explains the evolution of text‑to‑image generation since 2022, discusses data preparation, model quality, prompt engineering, multi‑style support, evaluation methods, and predicts when fully AI‑generated video and movies might become mainstream.
The article reverse‑engineers GitHub Copilot’s VSCode extension, detailing how its webpack‑bundled JavaScript is unpacked, its registerGhostText entry point identified, and its prompt‑building, multi‑layer caching, debouncing, and Jaccard‑based similarity mechanisms operate, offering insights into AI‑assisted code completion design.
This article investigates how large language models can be applied to recommendation tasks, presenting two usage strategies, experimental evaluations on multiple datasets, comparisons with traditional baselines, and analyses of prompting methods, cost, and cold‑start performance.
This article explains how large language models can be used to convert natural language queries into SQL statements, describes two main approaches—direct generation and fine‑tuned open‑source models—details prompt engineering techniques, and outlines an end‑to‑end pipeline that executes the generated SQL and summarizes results.
This guide introduces Tencent Hunyuan's upgraded text‑to‑image model, explains its technical innovations, provides detailed prompt engineering advice, showcases example prompts and generated images across various styles, and highlights real‑world applications and performance metrics for developers and creators.
The article explains prompt engineering techniques, supervised fine‑tuning of large language models, and their practical deployment in the Mobile Tmall AI shopping assistant, detailing ChatGPT’s generation steps, Transformer architecture, prompt clarity, delimiters, role‑play, few‑shot and chain‑of‑thought prompting, SFT versus pre‑training, LoRA adapters, data collection, Qwen‑14B training configuration, SDK‑based inference, and comprehensive evaluation.
This article shows programmers how to harness ChatGPT beyond simple Q&A by using advanced prompting techniques for knowledge summarization, task decomposition, code reading, refactoring, generation, unit‑test creation, and plugin integration, turning AI into a practical development assistant.
The article examines how rising code volume and AI‑generated snippets challenge traditional code review, proposes an LLM‑assisted workflow using AutoDev and DevOpsGenius, details prompt design, commit filtering, and implementation steps, and discusses the benefits and limitations for different team roles.
This article presents Nine Chapters Cloud Extreme DataCanvas's insights and practices on multimodal large model platforms, covering their historical development, platform components such as AI Foundation Software and Prompt Manager, practical implementations like memory-augmented models and ETL pipelines, and future prospects for enterprise knowledge bases and agents.
This article analyzes how carefully crafted prompts influence the architecture of complex AIGC applications, comparing tools like GitHub Copilot, JetBrains AI Assistant, and Bloop, and presents practical strategies and design patterns for building robust, context‑aware AI‑driven development environments.
This article describes how to construct an intelligent customer‑service assistant using ChatGPT for natural‑language understanding, BERT for user‑question classification, and Sentence‑BERT for knowledge‑selection, detailing system architecture, prompt design, model training, performance results, and practical cost reductions.
This article describes the design and implementation of a code‑review assistant powered by open‑source large language models and a privately hosted knowledge base, covering background, pain points, system architecture, model selection, vector‑store integration, prompt engineering, diff parsing, and practical reflections.
This article explores the design principles, architectural decisions, and practical code examples behind the Chocolate Factory framework, a JVM‑centric LLM development platform inspired by LangChain, LlamaIndex, Spring AI, and PromptFlow, highlighting SDK construction, RAG workflows, and prompt engineering challenges.
This article provides an introductory overview of large language models (LLMs), compares popular models, explains their underlying principles, and offers practical guidance on prompt engineering, model evaluation, usage tips, and safety considerations, helping readers effectively select and apply LLMs in various scenarios.
This article demonstrates how to use GPT to accelerate code development, automate script creation, and integrate AI-driven suggestions into real‑world business scenarios such as e‑commerce promotion, providing practical tips, example prompts, and ready‑to‑run code snippets.
This article explains the technical workflow behind Baidu App’s AI‑assisted text generation, covering large‑language‑model fundamentals, three‑layer system architecture, prompt design, risk‑control measures, SSE streaming, and a custom Android TextView implementation for smooth, gradient‑styled output.
The article surveys how large language models can be integrated into recommendation systems, either as feature extractors or as end‑to‑end recommenders, showing that LLM‑derived semantics improve recall, ranking, diversity, and user experience, and outlining future multimodal, efficiency, and re‑ranking directions.
This article offers a non‑algorithmic engineer’s clear overview of large language models, explaining ChatGPT’s generative‑pre‑training‑transformer foundation, core mechanisms like attention, practical prompt‑engineering tips, and how enterprises can integrate LLMs into data analysis, smart‑customer service, and other business workflows while noting associated risks.
The article describes how the author used ChatGPT to generate over 5,000 lines of code for a WeChat mini‑program community app, outlining the AI‑Driven Development (AIDD) workflow, detailed login and post‑list module implementations, multiple iteration cycles, code snippets, and reflections on efficiency and reuse.
This article walks through constructing a domain‑specific, soul‑healing chatbot using LangChain and Llama 2, comparing fine‑tuning versus external knowledge bases, detailing environment setup, data loading, text splitting, embedding with a Chinese model, vector store creation, prompt engineering, inference, and optimization strategies.
This article examines how large language models and knowledge graphs complement each other, discussing their respective strengths, integration techniques such as prompt engineering and knowledge editing, and outlining future research directions for building large knowledge models that combine linguistic understanding with structured knowledge representation.
This article compares Prompt Engineering and Supervised Fine‑Tuning for large language models, explains their principles, showcases common prompt patterns such as Chain‑of‑Thought, ReAct and Self‑Ask, outlines fine‑tuning stages and trade‑offs, and provides practical guidance on selecting the most suitable approach for specific enterprise AI Agent scenarios.
This article walks you through using Midjourney to design custom emoji packs, covering style selection, prompt crafting, seed control, blind‑box generation, and post‑processing, while highlighting how AI can dramatically boost creative efficiency for designers.
This article shares practical insights from months of building LLM proof‑of‑concepts, covering language‑agnostic architectures, FFI integration, prompt engineering, RAG patterns, DSL design, and four core architectural principles for scalable AI applications.
The article introduces the ChatGPT 0720 update, explains the new Custom Instructions feature, shows how to enable and configure it, compares responses with and without the feature, and provides detailed code examples for implementing similar functionality via system messages in developer tools.
This article examines how the rapid rise of generative AI reshapes application engineering by outlining AI's core characteristics, the challenges developers face, the evolution of prompt and chain-of-thought techniques, the emergence of agents and tool integration, and the future direction toward AI‑centric computing architectures.
The article examines the rapid rise of AI engineers, defines their responsibilities, compares them with traditional ML engineers, analyzes market demand and challenges, and outlines practical steps for aspiring professionals to acquire the skills and experience needed for this emerging role.
Learn a low‑cost, high‑security method to generate multiple personalized AI digital avatars from a single portrait by using Stable Diffusion WebUI, img2img inpainting, and the ControlNet openpose plugin, covering prompt design, image masking, and step‑by‑step workflow without uploading data to external servers.
The guide explains GPT’s core capabilities—natural language mastery, domain reasoning, and code generation—while detailing its limits such as prompt sensitivity, token caps, and lack of memory, then offers engineering workarounds like systematic prompting, chain‑of‑thought, external memory, tool integration, safety checks, and a six‑layer architecture for building robust commercial AI applications.
This guide shows how to quickly generate Qixi-themed visual assets with the AI tool “Instant Inspiration”, covering model selection, prompt crafting, style variations, and post‑processing steps to streamline the entire design workflow.
The guide outlines prompt‑engineering best practices for large language models, distinguishing base and instruction‑tuned LLMs, emphasizing clear, structured, step‑by‑step prompts, handling hallucinations, iterating through idea‑code‑data cycles, applying techniques to summarization, reasoning and expansion, managing token costs, and providing concrete OpenAI API examples.
This article outlines a practical enterprise architecture for integrating large language models into a sales assistant, detailing knowledge ingestion, vector embedding, task planning, tool usage, and iterative dialogue, while introducing AI Agent concepts and open‑source frameworks such as LangChain.
This article details how Zhihu's internal Bridge platform integrates large language models for business analysis, knowledge taxonomy, natural‑language‑to‑filter conversion, and ad‑hoc data queries, describing the workflow, technical hurdles, iterative improvements, and future directions.
This article examines how large language models (LLMs) are reshaping enterprise applications by enabling natural‑language interfaces, automating workflows, and enhancing data analysis, while also detailing typical use cases, integration complexities, and practical solutions such as prompt engineering and vector‑database retrieval.
This practical guide walks users through installing Stable Diffusion WebUI, explains the differences between base, LoRA, VAE, and ControlNet models, shows how to derive prompts with CLIP or DeepBooru, and provides detailed text‑to‑image and image‑to‑image examples for effective prompt engineering.
This tutorial introduces LangChain, an open‑source Python framework that provides unified model access, prompt management, memory, retrieval, and tool integration, enabling developers to quickly prototype AI‑driven applications using large language models and various external data sources.
The article surveys prompt engineering for large language models, detailing frameworks such as ICIO, BROKE, CRISPE, TRACE, COT and ToT, outlines best‑practice principles, provides diverse template examples, addresses common response issues with mitigation tactics, and lists learning resources and a Java refactoring case study.
This article explores how Stable Diffusion can revolutionize visual design by instantly converting line sketches into 3D scenes, turning flat cartoon IPs into detailed 3D characters, and generating creative font styles, while detailing the required models, ControlNet settings, prompts, and post‑processing techniques.
This article explains how integrating large language models into industrial configuration tools creates AI‑driven features such as knowledge Q&A, automatic application generation, smart drawing, script and material generation, and outlines the three‑layer architecture, prompt engineering, and implementation lessons for developers.
This article explores the practical deployment of ChatGPT‑based products, detailing the model fundamentals, technical architecture, engineering‑focused prompt design, real‑world application scenarios, and the challenges of model generalization, resource consumption, data privacy, interpretability, and ethical considerations.
This article provides a detailed overview of large language models (LLMs), tracing their evolution from statistical and neural language models to modern pre‑trained transformers, discussing scaling, training, adaptation, utilization, evaluation methods, available resources, and outlining current challenges and future research directions.
This article introduces prompt engineering for ChatGPT, explains key design principles, and demonstrates a series of practical applications such as text classification, summarization, role‑playing, terminal emulation, output formatting, temperature control, iterative fine‑tuning, and reverse‑engineering of prompts.
This guide shows how to bypass complex keyword prompts by selecting and combining AI style models in the free‑to‑use "Instant AI Inspiration" tool, covering model selection, workflow steps, practical examples, and tips for resolution, downloading, and unlimited usage.
This article introduces LangChain, explains its modular architecture—including models, prompts, memory, chains, and agents—shows how to handle tokens and model selection, and provides complete Python code examples for building a conversational retrieval chatbot with OpenAI models.
This comprehensive guide explores prompt engineering for large language models, covering its background, fundamental concepts, prompt formats, construction principles, advanced techniques like few‑shot, zero‑shot, and chain‑of‑thought prompting, as well as practical examples, evaluation metrics, and future directions.
This article analyzes the evolving landscape of large‑model applications, detailing prompt engineering, engineering challenges, AI‑engineer roles, domain‑driven design, and agent frameworks, while offering practical guidance and references for building production‑grade LLM‑driven systems.
The interview with ZhongAn Insurance CTO Jiang Jiyun discusses how the company leverages AIGC technologies such as large language models, embeddings, and prompt engineering to enhance marketing, intelligent customer service, and data security, while highlighting practical challenges and best practices for AI adoption in the insurance sector.
This article reviews how recent large language models (LLMs) are reshaping recommendation systems, covering their emergence, in‑context learning, prompt‑based strategies, three main LLM‑driven recommendation paradigms, key research papers, experimental results, and future research directions.
The article examines the surge of generative AI, outlining its potential to democratize software interaction through natural‑language prompts while highlighting challenges such as prompt‑engineering complexity, the opaque nature of large language models, and the risk of marginalizing users lacking literacy or GUI‑based control.
This article explains the limitations of large language models, introduces prompt engineering as a remedy, and provides a comprehensive guide to using the LangChain framework—including models, prompts, chains, agents, vector search, and practical Node.js code examples—to enable LLMs to interact with external tools and data sources.
This article introduces LangChain—a modular framework for constructing large‑language‑model applications—covering its core components, asynchronous support, prompt engineering, memory handling, chain and agent workflows, token considerations, embedding techniques, and a step‑by‑step Python example that culminates in a Gradio‑based conversational chatbot.
LangChain is an innovative framework that simplifies building language‑model‑driven applications by providing modular components such as models, prompts, memory, chains, and agents, with examples of asynchronous LLM calls, custom prompt templates, vector stores, token handling, and a simple Gradio chatbot implementation.
Microsoft's newly released Guidance language unifies generation, prompting, and logical control for large language models, enabling seamless integration with providers like Hugging Face, enhancing prompt reliability, and simplifying complex LLM workflows for developers.
