Everything You Need to Know About AI in 2026

Generative AI creates new content such as images, text, videos, and audio, while agentic AI uses autonomous agents to plan, decide, and execute tasks.

Explore core LLM concepts from inference and training to prompts, temperature, top p, guardrails, tokens, and context, including the model context window up to 400,000 tokens.

Delve into neural networks and deep learning, learn how knowledge bases, vector embeddings, and text chunking empower llms to retrieve information through memory and context using transformers.

Explore how knowledge bases store internal and external information for llms and why they enhance response relevance through retrieval-augmented generation (rag) and knowledge graph document store embeddings.

Explore vector embeddings that convert unstructured data into numerical vectors, enabling efficient retrieval and semantic similarity in LLMs, with tools like Pinecone to store vectors in a database.

Chunk text into manageable pieces to fit the model's token limit and enable embeddings, then use vector search to retrieve the most relevant chunks for accurate answers.

Memory in LLMs stores and recalls information across conversations to personalize interactions and improve reasoning, using buffer memory, entity memory with knowledge graphs, and external memory via vector databases.

The lecture explains how LLMs retrieve information from internal and external sources using keyword and vector search, including vector embeddings and cosine similarity, and how hybrid search blends the two.

Explore AI agents as autonomous systems that plan, reason, and act to achieve goals using memory, tools, and APIs. Advance from simple reflex to multi-agent frameworks.

Shift from consumer AI to building deployable AI products by designing autonomous AI agents and production chatbots using retrieval augmented generation, knowledge bases, and semantic search with Lang chain.

Explore LangChain, an open-source orchestration framework that connects LLMs with tools, prompts, memory, and agents through chains to enable multi-step tasks like document summarization, question answering, and API automation.

Learn how retrieval augmented generation (rag) grounds language models in your data, using embeddings and vector databases for fast semantic search to reduce hallucinations and produce reliable, domain-specific AI answers.

Master industry-ready rag applications by addressing scaling retrieval with partitioning, hierarchical indexing, and hybrid semantic-keyword search. Implement incremental updates, robust ingestion, and hybrid evals to ensure faithfulness and relevance.

Explore building rag Q&A agents in the real world from data, with and without code, using templates, embeddings, and retrieval chains for fast, accurate answers.

Agentic rag extends retrieval augmented generation with autonomous agents that plan, reason, and act, orchestrating retrieval, tool use, and web and vector searches for robust multi-hop decision making.

Compare traditional rag and agentic rag architectures, showing how an aggregator agent reasons, plans, and retrieves data from memory, databases, and vector embeddings to query LLMs.

Fine-tune pre-trained models to adapt to specific tasks, covering supervised fine tuning, transfer learning, few-shot learning, and human-in-the-loop reinforcement learning tuning.

Explore reinforcement learning from human feedback, where human in the loop provides reward signals to fine-tune the model, enabling product development and understanding prompt types.

Master expert tips for effective prompting by defining tasks and outcomes, assigning roles, providing full context and output instructions, and using rewards, while avoiding ambiguity and iterating to refine prompts.

Explore single-turn versus multi-turn fine-tuning, comparing isolated question-answer prompts to conversational memory-enabled interactions for tasks like classification, summarization, and chatbots.

Master context engineering, designing and optimizing the full inputs for an LLM during inference—memory, state, context window, Rag, tools, and data—beyond prompts alone.

Join a hands-on guide to fine-tuning an OpenAI model. Learn to generate training data, craft JSONL prompts, upload, train, and test a fine-tuned model.

Explore how n8n ai automation enables hyper-personalized email outreach by connecting Gmail, Google Sheets, and OpenAI through json-based workflows built from templates.

Design robust AI workflows and deploy them to production with rigorous testing, RBAC, version control, and monitoring; configure triggers, secrets, and performance metrics for scalable, reliable automation.

Build an n8n workflow from scratch to generate travel itineraries and send them by email, using triggers, actions, utility nodes, AI agents, and memory.

Explore model context protocol (MCP), an open standard that lets LLMs connect to tools and external systems, enabling dynamic tool use, interoperability, and persistent memory.

Learn to evaluate LLMs using metrics like accuracy, relevance, coherence, perplexity, faithfulness, and bias, and apply LM-as-a-judge or human-in-the-loop with golden data sets.

Explore OpenAI's Agent Kit, including the visual Agent Builder and Chat Kit, to create, deploy, and optimize AI agents with guardrails, evals, and memory.

Explore how the agent builder configures an agent with start and end nodes, memory, tools, guardrails, and MCP connections to create robust workflows.

Explore building a document comparison and question-and-answer agent with agent kit, using rag and multi-agent workflows to compare, unify, and reconcile two documents.

Build a structured data question/answer agent with agent kit to translate natural language queries into executable sql via domain classification for commerce and personal and guardrails.

discover how to build an agent kit that converts youtube videos into personalized linkedin posts in your voice, using guardrails, web search, and user approval for posting.

Explore how agentic and autonomous AI shifts from assistive tools to independent agents, enabling multi-step workflows, superhuman reasoning, personalized healthcare, and immersive multimodal experiences.

Apply responsible ai by enforcing fairness, transparency, accountability, privacy, and safety. Build with guardrails, data audits, diverse training, and model cards to prevent bias and discrimination.

Continue learning and building with AI to maintain your edge in a fast-moving field, stay updated with tools and leaders like OpenAI and Anthropic, and share your AI projects.