#prompt-engineering (91)
- Mastering Loop Engineering: Building Autonomous AI Agent Workflows
Explore loop engineering, the evolution from prompt engineering, to build production-grade autonomous AI agent workflows with goal-driven loops, tool use, and human oversight.
- Introduction to Loop Engineering: The Autonomous Agent Paradigm
Explore Loop Engineering as the evolution of prompt engineering, enabling AI agents to execute goal-driven workflows with tool access, feedback, and human checkpoints.
- Architecting Robust AI Agent Execution Loops
Learn to architect production-grade AI agent execution loops, integrating tools and human oversight for robust, goal-driven autonomous workflows.
- Engineering Autonomous AI Agents with Iterative Loops
Learn to architect and build production-grade autonomous AI agents by orchestrating continuous, goal-driven behaviors with iterative execution loops and human oversight.
- Loop Engineering: Autonomous AI Agent Workflows
Explore Loop Engineering, the next evolution after prompt engineering. AI agents build autonomous, production-grade coding workflows via goal-driven execution, tools, and human oversight.
- Build Control Systems to Guide AI Agent Actions and Tool Use
Implement effective control systems for AI agents to guide their actions, manage tool usage, and ensure reliable, predictable behavior.
- Agent State Management: Keeping Track of Context and Progress
Learn how to manage an AI agent's state, context, and progress systematically to ensure reliable and consistent behavior across interactions and complex tasks.
- Context Engineering: Optimizing Prompts and Tool Definitions
Master Context Engineering for AI coding agents: learn to optimize prompts and define tools effectively, ensuring your agents receive clear, actionable instructions and perform reliably.
- Develop Long-Term Memory for AI Agents with Knowledge Retrieval
Learn to equip AI agents with long-term memory by leveraging external knowledge bases, vector databases, and Retrieval Augmented Generation for robust performance.
- Building Multi-Stage Markdown Agents for Complex Workflows
Learn how to design and implement multi-stage markdown agents using AIPack, leveraging Lua for dynamic control flow and building robust AI workflows.
- Context Control and Large Codebases: Managing Agent Memory
Master context control in AIPack to manage AI agent memory effectively, especially when working with large codebases. Learn RAG, chunking, and dynamic context loading.
- Analyzing the Gay Jailbreak: LLM Security Vulnerabilities
Explore the Gay Jailbreak technique to grasp LLM prompt injection vulnerabilities and develop robust, context-aware mitigation strategies.
- Build Context-Aware Systems with MCP and TypeScript
Master the Model Context Protocol to design, implement, and deploy robust context-aware systems using the official TypeScript SDK effectively.
- Solving the AI Context Problem with Model Context Protocol (MCP)
You will understand the critical context problem for AI tools and how the Model Context Protocol provides a robust, standardized solution.
- Solving the AI Context Problem with Model Context Protocol (MCP)
You will understand the critical context problem for AI tools and how the Model Context Protocol provides a robust, standardized solution.
- Defining Context: MCP Schemas, Data Models, and Dynamic Negotiation
Explore how Model Context Protocol uses JSON Schema to define structured context, design custom data models, and implement dynamic negotiation between clients and servers for robust intelligent tools.
- MCP Core Protocol Messages, Context Lifecycle, State Management
Understand the core Model Context Protocol message types, context session lifecycle, and state management to build robust, intelligent applications.
- Defining Context: MCP Schemas, Data Models, and Dynamic Negotiation
Explore how Model Context Protocol uses JSON Schema to define structured context, design custom data models, and implement dynamic negotiation between clients and servers for robust intelligent tools.
- MCP Core Protocol Messages, Context Lifecycle, State Management
Understand the core Model Context Protocol message types, context session lifecycle, and state management to build robust, intelligent applications.
- Build Your First MCP Client with TypeScript SDK
Learn to build your first Model Context Protocol client using the TypeScript SDK, send structured context, and apply client-side best practices.
- Build Your First MCP Client with TypeScript SDK
Learn to build your first Model Context Protocol client using the TypeScript SDK, send structured context, and apply client-side best practices.
- Build a TypeScript MCP Server for AI Context
Build a TypeScript MCP server to define and resolve dynamic context, empowering intelligent AI tools with relevant, real-time data.
- MCP Extensions: Diving into MCP Apps and Crafting Custom Solutions
Explore MCP extensions, focusing on the MCP Apps specification, and learn to design and implement custom context solutions for dynamic tool interaction.
- Build a TypeScript MCP Server for AI Context
Build a TypeScript MCP server to define and resolve dynamic context, empowering intelligent AI tools with relevant, real-time data.
- MCP Extensions: Diving into MCP Apps and Crafting Custom Solutions
Explore MCP extensions, focusing on the MCP Apps specification, and learn to design and implement custom context solutions for dynamic tool interaction.
- Adapting Prompts for Claude Opus 4.7 System Changes
Learn to adapt your prompt engineering strategies for Claude Opus 4.7's updated system prompt, ensuring optimal model behavior and avoiding regressions.
- Prompt Engineering and Agentic AI for Production
Learn to build and deploy advanced AI applications using prompt engineering and agentic AI workflows, focusing on practical, production-ready techniques for developers.
- Structure LLM Prompts for Precision and Reliability
Learn to craft secure and reliable prompts using system messages, delimiters, and structured output control for predictable LLM interactions.
- Advanced Reasoning with Chain-of-Thought and Self-Consistency
Unlock robust LLM reasoning with Chain-of-Thought and Self-Consistency. Learn to guide LLMs through complex problems, improving accuracy and reliability in production AI applications.
- Build Your First RAG System Using Embeddings and Vector Databases
Readers will build a RAG system, integrating document chunking, text embeddings, and vector databases to enhance LLM knowledge with external data.
- Evaluate & Test AI Prompts & Agents for Performance
Learn to rigorously evaluate and test your prompts and AI agents for accuracy, reliability, cost-efficiency, and safety in production environments.
- Prompt Engineering Fundamentals for Effective LLM Communication
Master fundamental prompt engineering techniques to effectively communicate with Large Language Models and build your first interactive AI applications.
- Orchestrating Agents with Frameworks: LangChain and LlamaIndex
Learn how to orchestrate complex AI agents using popular frameworks like LangChain and LlamaIndex, integrating LLMs, tools, and memory for production-ready applications.
- Introduction to Retrieval-Augmented Generation (RAG)
Grasp Retrieval-Augmented Generation fundamentals, see its value for production LLM applications, and build a functional RAG system using Python.
- Build AI Agents with Persistent Short-Term and Long-Term Memory
Learn to equip AI agents with persistent short-term context and long-term knowledge, making them more capable, consistent, and useful in applications.
- Production Deployment: Scaling, Cost Optimization, and Ethical AI
Take your AI agents from prototype to production. Learn critical strategies for scaling, optimizing costs, and ensuring ethical and responsible deployment of your agentic AI applications.
- Mastering Production Prompt Engineering & Agentic AI
Master prompt engineering & agentic AI for developers. This 2026 guide focuses on real-world production workflows, taking you from beginner to mastery.
- Understanding AI Agent Memory Systems: A Practical Guide
Explore the essential role of memory in AI agents, covering different memory types, storage, retrieval, and how agents use them to learn and maintain context.
- AI Coding Systems: From Copilots to Agents
Learn to leverage AI coding systems like Cursor 2.6 and GitHub Copilot to enhance your development workflow, from code generation and debugging to advanced agent-based automations.
- Ensuring AI Reliability: Evaluation and Guardrails
Learn to test, validate, and implement robust guardrails for AI systems, covering prompt testing, hallucination detection, and production-grade safety strategies.
- Design and Optimize Context for Large Language Models
Master context engineering to design, structure, and optimize input for Large Language Models, enhancing their performance and output quality.
- Short-Term Recall: Managing Agent Context and Conversation Memory
Explore short-term memory in Agentic AI systems, focusing on LLM context windows, conversation history management, and practical Python implementations for building coherent, stateful agents.
- How Agents Think: Designing Planning and Task Decomposition
Dive deep into the planning and task decomposition mechanisms that enable autonomous AI agents to break down complex goals into manageable steps. Learn how agents think and strategize.
- Persistent Memory & Context Management: Remembering the Past
Explore how AI agent frameworks manage short-term and long-term memory, and track workflow state to build intelligent, conversational, and persistent applications.
- Working, Short-term, and Long-term Memory in AI Agents
Learn how Working, Short-term, and Long-term Memory enable AI agents to maintain context, learn, and overcome LLM limitations in practical designs.
- Introduction to AI Agent Memory: Why Agents Need to Remember
Explore the fundamental need for memory in AI agents, understanding how it overcomes LLM limitations and enables more intelligent, stateful, and personalized interactions.
- Debugging AI: Pinpointing Issues in Prompts, Models, and Data
Learn how to effectively debug AI systems in production by pinpointing issues in prompts, model behavior, and data, using practical observability techniques and OpenTelemetry.
- Introduction to AI Guardrails: Principles & Architecture
Explore the fundamental principles and architectural patterns for building robust AI Guardrails, ensuring safety, reliability, and ethical behavior in production AI systems.
- Systematic Prompt Testing for LLM Performance and Safety
You will learn to systematically test and validate prompts for LLMs, ensuring your AI applications achieve optimal performance, safety, and reliability.
- AI System Evaluation and Guardrails Guide
Ensure AI system reliability with this guide on testing, validation, and guardrail design. Learn prompt testing, hallucination detection, output validation, and real-world production strategies.
- Building a Secure LLM Interaction Layer in Python
After this, you can build a secure Python interaction layer for LLMs, protecting against prompt injection and insecure outputs with defense-in-depth.
- AI as Your Debugging Partner: Error Analysis and Fix Suggestions
Master AI-powered debugging with GitHub Copilot and Cursor 2.6. Learn to analyze errors, get fix suggestions, and leverage AI agents for efficient troubleshooting in Python.
- Generate Tests and Validate Code with AI-Driven Testing
Learn to integrate AI coding systems into your testing workflow to generate tests, validate code, and apply best practices for robust software.
- Best Practices for AI-Augmented Development: Security, Ethics, and IP
Master best practices for secure, ethical, and IP-conscious AI-augmented development with tools like Cursor 2.6 and GitHub Copilot.
- AI Agents and Automations to Streamline Dev Workflows
Learn to leverage AI agents and automations for proactive task management and streamlined software development workflows.
- Generate Functions, Classes, and Files with AI Coding Tools
Learn to use AI coding assistants like Cursor and GitHub Copilot to generate complete functions, classes, and files, improving your development workflow.
- Your First AI-Generated Code: Inline Suggestions and Autocomplete
Discover how to leverage AI coding assistants for inline code suggestions and intelligent autocomplete to boost your development speed and efficiency.
- Automating CI/CD with AI Agents and Coding Systems
Automate your CI/CD pipelines with AI agents, streamlining tasks like code generation, testing, and deployment to significantly improve development efficiency.
- Mastering the AI Conversation: Prompt Engineering for Code
Unlock the full potential of AI coding tools like Cursor and GitHub Copilot by mastering prompt engineering for code generation, debugging, and advanced agent tasks.
- Setting Up Cursor 2.6 and GitHub Copilot for AI Coding
Learn to set up and configure Cursor 2.6 and GitHub Copilot, covering prerequisites, installation, and initial settings to enhance your AI-powered coding workflow.
- Welcome to AI-Augmented Development: Copilots vs. Agents
Explore the transformative world of AI-augmented development, differentiating between interactive AI copilots and autonomous AI agent systems, and understand their roles in modern coding workflows.
- Making Every Token Count: Context Reduction & Summarization
Learn how to optimize LLM context by mastering reduction and summarization techniques, enhancing performance and reliability in production AI systems.
- Optimize LLM Agent Context with Prioritization & Sliding Windows
Learn to implement dynamic context prioritization and sliding windows to manage LLM agent memory and overcome context window limits.
- Structuring Information for LLMs: Effective Context Design
Dive into effective context design for LLMs, learning how to structure information, manage data flow, and optimize inputs for superior AI performance and reliability.
- Beyond the Prompt: Building Multi-Source Context Pipelines (RAG)
Explore Retrieval-Augmented Generation (RAG) to overcome LLM limitations, integrate external knowledge, and build dynamic, multi-source context pipelines for production AI systems.
- The Core of LLM Intelligence: What is Context Engineering?
Dive into Context Engineering for AI systems, understanding how to design, structure, and optimize context to enhance LLM performance, reliability, and cost-efficiency in production environments.
- Production-Ready Context: Best Practices & LLMOps
Master production-ready context management for LLMs. Learn best practices for designing, structuring, and optimizing context within LLMOps workflows to enhance AI reliability and performance.
- Breaking Down Information: Smart Chunking Strategies
Master smart chunking strategies to effectively break down large documents for LLMs, improving context management, relevance, and RAG system performance.
- Navigating the LLM's Memory: Understanding the Context Window
Dive deep into the LLM's context window, understanding its mechanics, limitations, and the critical role of tokenization in managing the LLM's 'short-term memory' for robust AI applications.
- Context Engineering for LLMs Guide
Master context engineering for LLMs. Learn reduction, compression, chunking, prioritization, and multi-source pipelines to optimize AI output quality in production systems.
- Move Beyond Simple RAG Chunking with Advanced Context Assembly
Learn to implement advanced context assembly techniques to overcome simple chunking limitations and build highly coherent contexts for RAG systems.
- Advanced Agent Personalization and Context Management
Learn advanced techniques for agent personalization and context management to create more human-like AI agents.
- Building a Real-World Customer Support Agent (Project 1)
Learn to build a multi-agent customer support system using OpenAI's Agents SDK, covering agent design, implementation, and testing.
- Crafting Conversations: Prompt Design & State Management
Learn how to design effective prompts and manage state in React applications for seamless AI interactions.
- Project: Creating a Context-Aware Copilot
Learn how to build a context-aware AI copilot using React and an AI API, enhancing user experience with intelligent responses.
- Advanced Prompt Engineering with Kiro
Learn advanced prompt engineering techniques for Kiro, the intelligent AI agent for AWS.
- Applied & Agentic AI: Build Intelligent Agents & Systems
Master the skills to design, build, and deploy sophisticated Applied and Agentic AI systems, creating intelligent agents for practical, real-world solutions.
- Mastering Prompt Engineering for Effective LLM Instruction
Learn the principles and practices of crafting effective prompts to guide Large Language Models for specific tasks and build intelligent AI agents.
- Build a Smart AI Research Assistant Agent Project
Design and implement a multi-agent AI system to build a smart research assistant that searches, synthesizes, and summarizes information.
- Applied and Agentic AI Development for Real-World Apps
Learn to build real-world Applied and Agentic AI applications, mastering foundational programming, advanced agent orchestration, and production deployment skills.
- Project: Summarizing and Structuring Financial Reports
Learn to build a LangExtract solution for extracting structured financial data from reports.
- Best Practices for Prompt Engineering with LangExtract
Learn advanced techniques for prompt engineering with LangExtract to achieve accurate data extraction.
- Common Pitfalls and How to Avoid Them
Learn to avoid common pitfalls in data extraction using LangExtract and Large Language Models.
- Appendix A: Advanced Prompting Techniques
Advanced techniques for crafting effective prompts to elicit high-quality responses from language models.
- Agentic Design Patterns: An End-to-End Guide
Learn to design, build, and deploy robust AI agent systems by mastering core agentic design patterns, enabling you to create intelligent, autonomous applications.
- Core Concepts: Prompts, Completions, and Parameters
Explains the core concepts of prompts, completions, and parameters in Large Language Models.
- Developing an LLM-Powered Content Summarizer (Hands-on Project)
Learn to build an LLM-powered content summarizer using Python and the any-llm library.
- Guided Project 2: Optimizing LLM Prompts with TOON
Learn how to optimize LLM prompts using TOON for cost reduction and improved performance.
- Advanced Optimization Algorithms
An overview of advanced optimization algorithms used in AI agent training, focusing on Reinforcement Learning and Automatic Prompt Optimization.
- Further Resources for Agentic Lightening & LLM Agents
Discover curated resources to deepen your understanding of Agentic Lightening, LLM agents, and stay updated with AI advancements.
- Build & Optimize a QA Agent with Automatic Prompt Optimization
Readers will learn to build and optimize a question-answering agent, enhancing its accuracy by dynamically tuning prompts with Agentic Lightening's APO.