Agentic AI & Prompt Engineering Bootcamp: Build AI Employees

Explore how large language models work as prediction engines that process tokens and patterns. Learn how prompts, context, and careful input drive accurate, less hallucinatory AI outputs.

Discover the anatomy of a perfect prompt by applying a four-part framework—role, task, context, and output—and using constraints and a reusable template to build reliable AI employees.

Improve AI results by mastering prompt input using the RTCO framework—role, task, context, and output—to turn vague prompts into precise, structured interactions.

Compare zero-shot and few-shot prompting, highlighting how structure, examples, and pattern-based generation shape output quality, consistency, and control. Choose the right technique for speed, precision, and task demands.

Unlock chain of thought, or cote, a prompt technique that reveals step-by-step reasoning to improve accuracy and transparency in multi-step tasks; use rtco for structure and reserve cote for analyses.

Master instruction engineering by shaping precise, actionable directives—defining role, task, constraints, and format—to control artificial intelligence output, using clarity, structure, and repeatable performance.

Shape length, tone, format, and style to make AI outputs reliable, repeatable, and ready for real-world system use.

Diagnose prompt issues, fix root causes, and iterate with the RTCO framework to produce production-ready instructions through structured testing and optimization.

Claude Code, an AI-powered coding assistant with a large context window, writes, edits, debugs, and builds software from natural language. Describe tasks, generate and refine code, and deploy production-ready apps.

Compare ChatGPT, Claude, and other ai tools to see no single best option. Choose tools by task, speed, reasoning, large context, and integrations to improve results.

Set up a robust development environment with VS Code, Node.js, and Python; master the terminal, package managers npm and pip, and verify your setup for smooth coding, running, and building.

Learn the basics of APIs, including the request-response cycle, endpoints, methods, headers, body, JSON data, security via API keys, and how to call APIs with fetch or requests.

Explore context as the brain behind AI, defining task, user, data, and conversation contexts, and show how clear, relevant context improves output and builds intelligent agents.

Explore how session memory and long-term memory empower AI agents to maintain continuity, personalize interactions, and improve responses across conversations.

Context files act as external memory, storing persistent information, guiding behavior, and loading data in real time to make AI responses adaptive and reliable.

Identify what AI hallucinations are—confident, incorrect outputs not grounded in data—and reduce them with better prompts, real-data grounding, verification, and output control for trustworthy, accurate results.

Explore how an ai agent perceives, reasons, and acts autonomously, moving beyond chatbots to get work done, guided by a five-component architecture—input, reasoning, memory, tools, and output.

Explore how agent behavior orchestrates actions, decisions, and communications by applying goals, rules, constraints, and decision logic to create reliable, predictable AI employees.

Define a prompt-based agent by detailing the user input, system prompt, and output, enabling zero setup, fast iteration, and clear behavior without tools, application programming interfaces, or external systems.

Design action selection, behavior flow, and the thinking layer to turn input into purposeful, context-aware actions. Learn how structured logic enables multi-step reasoning, reliable automation, and controlled decisions.

Activate event-driven systems that listen for events, trigger workflows, apply logic, and act instantly to deliver fast, efficient, scalable real-time automation for AI agents.

Cron jobs automate time-based tasks on a fixed schedule without manual intervention, enabling reliable always-on workflows. Define cron expressions to schedule tasks like daily reports, backups, and email reminders.

Background execution powers always-on, autonomous systems by running tasks independently in the background, enabling 24-7 automation, scalability, and non-blocking performance for AI agents and workflows.

Learn how trigger-based workflows turn events into fully automated end-to-end processes using trigger, logic, and actions, enabling AI employees across event-based, time-based, and manual triggers.

Agent communication coordinates multi-agent systems by sharing results, context, and state through message passing, shared memory, or API calls, using structured data to keep agents aligned and reliable.

Explore task delegation in multi-agent systems, breaking down complex goals into subtasks, assigning them to specialized agents, and synthesizing outputs into a cohesive final output.

Workflow orchestration serves as the system control layer, coordinating planning, execution, monitoring, and adjustment via the orchestrator to route tasks and enable error handling, producing a reliable, scalable production-ready system.

Design end-to-end AI systems by integrating prompts, memory, logic, and automation across input, processing, orchestration, and output to deliver scalable, reliable real-world solutions.

Explore how LLMs rely on static knowledge with a knowledge cutoff and hallucinations, then apply retrieval augmented generation to ground answers with external data for accuracy.

Learn how retrieval augmented generation (rag) uses a simple three-step cycle—ask, retrieve, and generate—to combine ai with real data for accurate, grounded, and reliable answers.

Connect AI to real data to deliver accurate, grounded answers across documents, databases, and systems. Learn a store-retrieve-use pipeline with embeddings and vector databases enabling retrieval-augmented generation and context injection.

Improve artificial intelligence answer accuracy by grounding responses in real data with retrieval augmented generation, precise prompting, high-quality context, and a verification layer within a layered, production-ready architecture.

Reduce hallucinations by grounding responses in real data through retrieval augmented generation (RAG), strong prompts, and validation layers. Build honest AI systems that admit uncertainty and constrain output for reliability.

The AI business analyst reads and understands reports to transform raw data into concise summaries, key insights, and recommendations in seconds via a three-stage understand-detect-generate workflow.

Discover the AI email automation agent that reads, understands, drafts, and auto-replies to emails in an end-to-end workflow, with natural language processing to boost productivity and reduce overload.

Empower teams with a fully autonomous AI task automation agent that runs 24-7, handling task execution, workflow automation, and background processing with zero manual input.