Generative AI Bootcamp 2026: ChatGPT, LLMs, Prompting

Explore how generative ai works as a pattern-based prediction engine, trained on vast human data, predicts the next token, and does not think, building fluent responses step by step.

Learn tokens, models, and training as the three core building blocks of generative AI. Understand how tokens read language, how the model processes input, and how training shapes outputs.

learn how text generation turns prompts into human-like writing through token-based, step-by-step prediction driven by context and probability, with emphasis on crafting clearer prompts for better responses.

Understand AI is powerful but not perfect, with limitations like hallucinations, overconfidence, bias, and memory constraints; verify outputs and rely on human judgment.

Explore large language models (LLMs) as the engines behind modern AI, trained on massive text data to understand and generate human-like language.

Generative AI bootcamp explores how ChatGPT and large language models use tokenization, context awareness, attention, and probabilistic next-token prediction to generate fluent, human-like responses.

Master tokenization and context windows to understand how AI processes text and remembers information. Learn to optimize prompts, balance token usage, and maintain context for reliable, cost-aware results.

Understand AI's strengths and limits to use it effectively. AI generates text, analyzes data, and speeds workflows, but memory, hallucination, and overconfidence require human judgment and verification.

Master prompt engineering to design clear, contextual instructions with audience, goal, format, and tone in mind, guiding AI to accurate, high-quality outputs.

Learn to craft clear, structured prompts with defined goals, context, and constraints, applying iteration and chunking to produce precise AI outputs.

Learn how role-based prompting shapes ai responses by assigning a role, audience, and depth to produce more relevant, structured outputs. Apply a four-element framework: role, context, format, constraints—for precise results.

Master chain-of-thought prompting to reveal step-by-step reasoning and improve accuracy and clarity. Use triggers, structured steps, and role-based prompts for complex tasks like math problems and logical reasoning.

Prompt templates turn AI into a scalable business system by providing reusable instructions with placeholders, driving consistency, speed, and scalable workflows across marketing, sales, operations, and customer support.

Embeddings convert text into numerical vectors that encode meaning, enabling AI to compare ideas mathematically. They power search, recommendations, chatbots, and retrieval-augmented generation by measuring semantic similarity with vectors.

Compare semantic search with keyword search by showing how embeddings map queries and documents into vectors, enabling meaning-based retrieval with cosine similarity and nearest-neighbor ranking.

Discover how vector databases store embeddings, perform similarity search, and enable fast, meaning-based retrieval for rag, semantic search, and recommendations.

Explore real-world AI applications across healthcare, finance, education, and retail, powered by embeddings, semantic search, vector databases, and retrieval augmented generation for AI assistants and content creation.

Rag combines retrieval and generation to ground responses in up-to-date external data. It uses embeddings and vector databases to retrieve, ground, and generate accurate, context-aware answers.

Discover how retrieval augmented generation fetches live external knowledge and domain-specific data to ground responses, using embeddings, vector search, and top-k retrieval for accuracy and reduced hallucinations.

Design smarter AI responses by combining better prompting, rich context, and retrieval to ground outputs in facts, with four building blocks: prompting, context, retrieval, and the model.

Compare RAG and fine-tuning for knowledge integration in modern AI systems, detailing runtime retrieval from external sources versus embedding knowledge during training, trade-offs, updates, and the hybrid approach.

AI agents move beyond question answering by planning, deciding, and executing end-to-end tasks. They use memory, tools, planners, and are goal-driven to manage multi-step workflows and real-world automation.

Master multi-step reasoning to transform simple prompts into autonomous, goal-driven workflows by thinking, acting, observing, and iterating with tools and verification.

Tool-using AI blends language generation with external tools to fetch live data and execute actions. A loop—decide, execute, return—uses APIs, databases, calculators, and file systems to turn intent into outcomes.

AI agents automate complete end-to-end workflows by reasoning, planning, and using tools to convert input tasks into autonomous, real world outcomes.

Set up your development environment for AI applications by installing Node.js and Python, configuring Visual Studio Code, organizing your project, managing dependencies, and securing API keys for model access.

Connect your app securely to external AI services using API keys for authentication and usage tracking. Extend your app with tools like APIs and databases to perform real-world tasks.

Explore the chatbot architecture as a multi-layer pipeline—from user interface and application logic to AI models and memory, incorporating retrieval-augmented generation and external integrations for scalable, context-rich conversations.

Define the chatbot goal and initialize a structured project scaffold. Build a modular backend, memory, and a minimal user interface to deploy a production-ready bot.

Improve artificial intelligence responses by optimizing prompts, context, grounding data, and feedback, using retrieval-augmented generation and structured prompts with iterative testing and metrics.

AI-driven automation lets businesses automate end-to-end processes, enabling AI decision making and smart operations across customer support, sales, operations, and human resources to reduce costs and boost accuracy and speed.

Leverage AI to transform marketing into a data-driven, automated engine that personalizes at scale, uses predictive models, and boosts targeting, engagement, and conversions.

Enable 24-7 instant, scalable customer support with ai-driven chatbots, smart routing, and personalization. Emphasize a balanced hybrid model, privacy, and continuous learning to improve speed and satisfaction.

AI acts as a developer's co-pilot, automating repetitive tasks, providing real-time code suggestions, code generation, and smart completion to accelerate delivery while reducing errors.

Analyze bias and hallucinations in ai systems to understand how data and training shape fair, transparent, and accountable outcomes, and apply layered mitigations like retrieval augmented generation and human-in-the-loop oversight.

Prioritize privacy from the start to build trustworthy AI by embedding privacy by design and safeguarding personal, sensitive, and behavioral data through strong controls.

Design, deploy, and govern AI with fairness, accountability, and transparency to ensure trustworthy, human-centered systems. Prioritize inclusive data, explainability, audits, and strong privacy to mitigate bias and protect users.

Learn how to deploy ai systems safely by prioritizing reliability, security, and monitoring; implement guardrails, pre-deployment testing, input validation, and continuous improvement to prevent data exposure, incorrect outputs, and outages.