Generative AI Mastery: Build, Automate & Scale with AI Agent

Set up ChatGPT on chat.openai.com, sign up or log in, and compare free vs plus, while exploring features like read aloud, edit in canvas, copy, share chats, and model selection.

Learn to craft effective prompts for chatbots by specifying clear goals, context, and audience, using chain-of-thought prompts, role-based templates, and concise length limits for better results.

Master prompting basics and prompt engineering with real life examples. Apply ChatGPT to learning, quizzes, writing, and idea generation.

Explore the five prompt types—instructional, conversational, narrative, comparative, and reflective—and learn how clear, specific prompts steer ai outputs, guiding dialogue, storytelling, and insights.

Create and reuse prompt templates to automate AI tasks, using variables and inputs to tailor stock price predictions across tools like Text Cortex.

Discover how AI-powered prompt engineering transforms business and marketing with automated customer support, scalable content creation, lead generation, and SEO.

Explore how AI powered by prompt engineering and intelligent prompting personalizes tutoring, supports homework, lesson planning, language learning, exam prep, reading comprehension, and accessibility across learning levels.

Explore future trends in prompt engineering, including programmatic and multimodal prompting. Discover domain-specific prompt kits and libraries like prompt layer shaping retrieval systems.

Explore how Gemini integrates search, Gmail, and Google Workspace to perform deep research, draft emails, and export documents to Google Docs, Google Sheets, and Microsoft Word, plus calendar events.

Explore practical tasks with Gemini to write emails using templates, automate replies, boost productivity with hacks, and generate ideas through writing and data analysis.

Gemini advanced contrasts Palm and Gemini 1.5, highlighting long context and coding focus. Palm is older at 540 billion parameters; Gemini 1.5 supports 4k tokens and MOE architecture with 2.5.

Master text generation and summarization with ai, using prompts to generate long, structured outputs for articles and seo content, with examples from rain and weather.

Learn to use Gemini in Google AI Studio to write, debug, and explain code in the browser without installation. Master Python data-scraping, error fixing, and app templates with pytest tests.

Explore how system prompts set AI behavior and user prompts drive direct queries, and learn real-world use cases that show how these prompts shape tone, accuracy, and output quality.

Discover what an api is, how client requests and server responses work, and how to make your first api call using Postman, an api key, and a Gemini endpoint.

Gemini in education shows how a multi-modal AI enhances learning, teaching, and research by enabling personalized tutoring, content summarization, language translation, and research synthesis.

Gemini for product management and analysis demonstrates how Google's multi-modal AI automates data collection, analyzes trends, and informs data-driven roadmaps with user feedback, feature ideas, and predictive insights.

Discover how Gemini writes, debugs, documents, and tests code from natural language prompts across multiple languages. Enhance productivity with contextual intelligence, code reviews, and documentation support.

Explore deepseek chat and coder, the open-source large language models for natural language understanding and code generation, covering chatbot use, coding, cost efficiency, and a copilot api.

DeepSeq, an open source language model, speeds up technical writing and software development by automating documentation, code generation, debugging, and testing, and integrates with IDEs and CI/CD pipelines.

Learn prompting techniques in DeepSeq chat to generate text, summarize, answer questions, build mind maps and tree visualizations, and prototype code with DeepSeq coder via reasoning.

Explores how DeepSeek's DeepThink R1 generates step-by-step reasoning through chain-of-thought prompts and structured problem solving, including math and Python coding examples.

Explore how to conduct self-guided research with DeepSea, generate efficient code, and tackle hypotheses and programming challenges—from general relativity concepts to Python solutions.

Explore how DeepSeek AI drives SEO optimization and content structuring, including keyword research, semantic SEO, topic clustering, outlines, internal linking, and alignment with EEAT guidelines for videos and websites.

Harness ai tools like DeepSeek for brainstorming, idea generation, and worldbuilding—creating stories, characters, plots, and tones via customizable styles.

Learn how deepseek coder generates, debugs, and explains Python code, tests with unit tests and pytest, adds docstrings and type hints, and uses role play prompts for clear explanations.

Explore how deepseek translates code across languages—python to javascript to sql—while simplifying and refactoring for readability, demonstrating how ai tools streamline cross-language coding.

Learn to use DeepSeq with Visual Studio Code by installing the extension, connecting a local DeepSeq, and working with GitHub Copilot and options for code generation, explanation, and privacy features.

Explore how GitHub Copilot acts as an AI-powered coding assistant that turns natural language into code, integrates with VS Code, and leverages CodeX with OpenAI, Gemini, and DeepSeek.

Install Visual Studio Code on Windows, add the GitHub Copilot extension, and start using Copilot to generate simple Python code.

Install and enable GitHub Copilot in VS Code, connect your account, and generate code. Debug and refine a simple calculator using Python, applying Copilot's fixes and comparing alternatives.

Leverage GitHub Copilot to debug and refactor code in Python projects, with real-time error detection and one-click fixes for a calculator app.

Compare GitHub Copilot, DeepSeq Coder, and ChatGPT for coding tasks using JavaScript, HTML, and CSS, assessing strengths, limits, and the fastest, most creative option.

Explore Perplexity AI, offering access to major AI models for 20 dollars, with voice chats, academic, social, and finance search modes, a spaces and templates library, and trusted news summaries.

Explore the difference between basic and pro perplexity plans, including feature access, file limits, and premium models like zbt 4.0 and grok 4, to decide the best fit.

Discover how sources, citations, and real-time web access power transparent and credible research using AI, with perplexity AI as an example.

Learn to cite with confidence using Perplexity AI, generating MLA, APA, and Chicago style references from academic sources. Compare styles side by side and verify sources via the sources tab.

Generate outlines and drafts for articles or essays using Perplexity AI; create outlines, mind maps, expansions, and MLA-cited rewrites to learn faster with structured knowledge.

Learn to use Perplexity AI for seo research, identifying keywords with lower difficulty and current trends. Refine generated content for tone and clarity, then publish seo-friendly posts that boost discovery.

Learn to craft effective research prompts for Paraplex ADI, defining purpose, topic, time-space scope, and output format to produce well-cited, focused results.

Build a personal knowledge workflow that gathers, organizes, and applies information through input, processing with summaries and highlights, stores in notes or mind maps, then reviews before applying.

Define your market research goal and refine questions using Perplexity AI. Build a structured analysis with segmentation, consumer behavior drivers, market size, sources, and competitive insights like BYD China.

Discover N8n's node-to-node visual automation and self-hosted data privacy. Master the core workflow of trigger, action, and logic, and explore nodes, canvas, execution, and expression within AI agent concepts.

Adopt ai automation to insert reasoning in the middle of workflows, translating meaning, not just words, and preserving context through an ai agent persona, bridging human creativity and digital execution.

Identify trigger node as the entry point that acts as the deterministic event authorizing the workflow. Without a successful trigger, downstream operations halt, like a rocket launch with no ignition.

Explore the workflow lifecycle by learning terms like draft, saved, active, pause, and error through a visual puzzle and real-world analogies.

Configure node parameters to control data processing, choosing between fixed and expression paths. Use fixed parameters for immutable data like emails, or expressions to personalize responses based on live input.

Track real-time automation with N8n by using the green toggle to activate workflows and monitor six execution statuses: waiting, executing, success, error, cancel, and timeout.

Securely connect n8n to third-party apps by using credentials as a digital vault, api keys, consent-based authentication via login redirects, basic authentication, and custom header tokens.

Learn how API authentication enables secure communication between N8n and an AI agent, using API keys, paid per-request services, and 100 free runs to build automated workflows.

Differentiate automation, AI automation, and AI agent across creational automation, reasoning, and autonomous action to solve complex problems using memory tools.

Discover how generative ai automates repetitive tasks to scale businesses, boost return on investment, and elevate human talent through 24-7 workflows that handle support, game keys, and bug reports.

Explore the N8n glossary to build a mental model of workflows, nodes, payloads, and executions, mastering triggers, actions, core and app nodes, webhooks, and http requests.

Build an end-to-end workflow that extracts form data via n8n, stores it in Google Sheets, and sends dynamic emails with Gmail integration.

Learn to build a conversational ai automation workflow by configuring a chatbot with an ai agent, system messages, and llm models (deepseek, grok chat), including memory and tools.

In the Generative AI Mastery course, explore how GitHub Copilot uses code and comments as input to predict the next code block, and how explicit context and examples improve prompts.

Explore chain of thought prompting with Copilot to break problems into steps, iteratively refine code, and solve checks like perfect squares with special cases 0 and 1.

Explore how GitHub Copilot uses role prompting and persona creation to guide code tasks through descriptive comments, naming conventions, and docstrings for task-specific results.

Explore data privacy, consent, ownership of ai-generated content, and how accountability, transparency, and ethical data sourcing shape copyright and fair use.

Explore the future of large language models by comparing OpenAI, Google, Meta, and DeepSeek. Examine multimodal capabilities, agent-like tasks, open source options, and ethical governance.