Learn the fundamentals of prompt engineering for ChatGPT. Learn effective prompting, and how to use LLMs for summarizing, inferring, transforming, and expanding.

ChatGPT Prompt Engineering for Developers

Use the powerful and extensible LangChain framework, using prompts, parsing, memory, chains, question answering, and agents.

LangChain for LLM Application Development

Learn and build diffusion models from the ground up, understanding each step. Learn about diffusion models in use today and implement algorithms to speed up sampling.

How Diffusion Models Work

Learn to break down complex tasks, automate workflows, chain LLM calls, and get better outputs from LLMs. Evaluate LLM inputs and outputs for safety and relevance.

Building Systems with the ChatGPT API

Create a chatbot with LangChain to interface with your private data and documents. Learn from LangChain creator, Harrison Chase.

LangChain Chat with Your Data

Create and demo machine learning applications quickly. Share your app with teammates and beta testers on Hugging Face Spaces.

Building Generative AI Applications with Gradio

Learn MLOps tools for managing, versioning, debugging, and experimenting in your ML workflow.

Evaluating and Debugging Generative AI

Learn to use LLMs to enhance search and summarize results, using Cohere Rerank and embeddings for dense retrieval.

Large Language Models with Semantic Search

Discover when to use finetuning vs prompting for LLMs. Select suitable open-source models, prepare data, and train & evaluate for your specific domain.

Finetuning Large Language Models

Learn Microsoft's open source orchestrator, Semantic Kernel and use LLM building blocks such as memory, connectors, chains and planners in your apps.

How Business Thinkers Can Start Building AI Plugins With Semantic Kernel

Learn how to accelerate the application development process with text embeddings for sentence and paragraph meaning.

Understanding and Applying Text Embeddings

Learn how to prompt an LLM to help improve, debug, understand, and document your code. Use LLMs to simplify your code and enhance productivity.

Pair Programming with a Large Language Model

Learn about the latest advancements in LLM APIs and use LangChain Expression Language (LCEL) to compose and customize chains and agents.

Functions, Tools and Agents with LangChain

Design and execute real-world applications of vector databases. Build efficient, practical applications, including hybrid and multilingual searches.

Vector Databases: from Embeddings to Applications

Learn how to evaluate the safety and security of your LLM applications and protect against risks. Monitor and enhance security measures to safeguard your apps.

Quality and Safety for LLM Applications

Learn advanced RAG retrieval methods like sentence-window and auto-merging that outperform baselines, and evaluate and iterate on your pipeline's performance. 

Building and Evaluating Advanced RAG

Get an introduction to tuning and evaluating LLMs using Reinforcement Learning from Human Feedback (RLHF) and fine-tune the Llama 2 model.

Reinforcement Learning From Human Feedback

Learn advanced retrieval techniques to improve the relevancy of retrieved results. Learn to recognize poor query results and use LLMs to improve queries.

Advanced Retrieval for AI with Chroma

Expand your toolkit with LangChain.js, a JavaScript framework for building with LLMs. Understand the fundamentals of using LangChain to orchestrate and chain modules.

Build LLM Apps with LangChain.js

Learn LLMOps best practices as you design and automate steps to fine-tune and deploy an LLM for a specific task.

LLMOps

Learn how to create an automated CI pipeline to evaluate your LLM applications on every change, for faster and safer development.

Automated Testing for LLMOps

Learn to build six applications powered by vector databases, including semantic search, retrieval augmented generation (RAG), and anomaly detection.

Building Applications with Vector Databases

Learn how to deploy an LLM-based application into production using serverless technology. Learn to prompt and customize LLM responses with Amazon Bedrock.

Serverless LLM Apps Amazon Bedrock

Learn best practices for prompting and selecting among Meta Llama 2 & 3 models. Interact with Meta Llama 2 Chat, Code Llama, and Llama Guard models.

Prompt Engineering with Llama 2&3

Learn how to easily build AI applications using open-source models and Hugging Face tools. Find and filter open-source models on Hugging Face Hub.

Open Source Models with Hugging Face

Learn how to build and use knowledge graph systems to improve your retrieval augmented generation applications. Use Neo4j's query language Cypher to manage and retrieve data.

Knowledge Graphs for RAG

Understand how LLMs predict the next token and how techniques like KV caching can speed up text generation. Write code to serve LLM applications efficiently to multiple users.

Efficiently Serving LLMs

Build a full-stack web application that uses RAG capabilities to chat with your data. Learn to build a RAG application in JavaScript, using an intelligent agent to answer queries.

JavaScript RAG Web Apps with LlamaIndex

Learn how to make safer LLM apps through red teaming. Learn to identify and evaluate vulnerabilities in large language model (LLM) applications.

Red Teaming LLM Applications

Improve your RAG system to retrieve diverse data types. Learn to extract and normalize content from a wide variety of document types, such as PDFs, PowerPoints, and HTML files.

Preprocessing Unstructured Data for LLM Applications

Learn how to quantize any open-source model. Learn to compress models with the Hugging Face Transformers library and the Quanto library.

Quantization Fundamentals with Hugging Face

Explore Mistral's open-source and commercial models, and leverage Mistral's JSON mode to generate structured LLM responses. Use Mistral's API to call user-defined functions for enhanced LLM capabilities.

Getting Started with Mistral

Learn prompt engineering for vision models using Stable Diffusion, and advanced techniques like object detection and in-painting. 

Prompt Engineering for Vision Models

Customize model compression with advanced quantization techniques. Try out different variants of Linear Quantization, including symmetric vs. asymmetric mode, and different granularities.

Quantization in Depth

Build autonomous agents that intelligently navigate and analyze your data. Learn to develop agentic RAG systems using LlamaIndex, enabling powerful document Q&A and summarization. Gain valuable skills in guiding agent reasoning and debugging.

Building Agentic RAG with Llamaindex

Build smarter search and RAG applications for multimodal retrieval and generation.

Building Multimodal Search and RAG

Automate business workflows with multi-AI agent systems. Exceed the performance of prompting a single LLM by designing and prompting a team of AI agents through natural language.

Multi AI Agent Systems with crewAI

Deploy AI for edge devices and smartphones. Learn model conversion, quantization, and how to modify for deployment on diverse devices. 

Introduction to on-device AI

Use the AutoGen framework to build multi-agent systems with diverse roles and capabilities for implementing complex AI applications.

AI Agentic Design Patterns with AutoGen

Build agentic AI workflows using LangChain's LangGraph and Tavily's agentic search. 

AI Agents in LangGraph

Interact with tabular data and SQL databases using natural language, enabling more efficient and accessible data analysis.

Building Your Own Database Agent

Learn to apply function-calling to expand LLM and agent application capabilities.

Function-calling and data extraction with LLMs

Train your machine learning models using cleaner energy sources.

Carbon Aware Computing for GenAI developers

Optimize the efficiency, security, query processing speed, and cost of your RAG applications.

Prompt Compression and Query Optimization

Learn the essential steps to pretrain a large language model from scratch.

Pretraining LLMs

Build and fine-tune LLMs across distributed data using a federated learning framework for better privacy.

Intro to Federated Learning

Learn Python programming with AI assistance. Gain skills writing, testing, and debugging code efficiently, and create real-world AI applications.

AI Python for Beginners: Basics of AI Python Coding

Learn how to securely fine-tune large language models (LLMs) with private data using federated methods, enhancing data privacy, minimizing risks of data leakage, and optimizing efficiency through Parameter-Efficient Fine-Tuning (PEFT) and Differential Privacy.

Federated Fine-tuning of LLMs with Private Data

Learn how to build embedding models and how to create effective semantic retrieval systems.

Embedding Models: from Architecture to Implementation

Systematically improve the accuracy of LLM applications with evaluation, prompting, and memory tuning.

Improving Accuracy of LLM Applications

Learn a flexible framework to build a variety of complex AI applications.

Building AI Applications With Haystack

Learn best practices for multimodal prompting using Google’s Gemini model.

Large Multimodal Model Prompting with Gemini

Build an interactive system for querying video content using multimodal AI

Multimodal RAG: Chat with Videos

Build faster and more relevant vector search for your LLM applications

Retrieval Optimization: Tokenization to Vector Quantization

Try out the features of the new Llama 3.2 models to build AI applications with multimodality.

Introducing Multimodal Llama 3.2

Efficiently handle time-varying workloads with serverless agentic workflows and responsible agents built on Amazon Bedrock.

Serverless Agentic Workflows with Amazon Bedrock

Build agents that collaborate to solve complex business tasks.

Practical Multi AI Agents and Advanced Use Cases with crewAI

Build systems with MemGPT agents that can autonomously manage their memory.

LLMs as Operating Systems: Agent Memory

Move your LLM-powered applications beyond proof-of-concept and into production with the added control of guardrails.

Safe and reliable AI via guardrails

Learn to build with LLMs by creating a fun interactive game from scratch.

Building an AI-Powered Game

Learn to use OpenAI Canvas to write, code, and create more effectively in collaboration with AI.

Collaborative Writing and Coding with OpenAI Canvas

Build LLM apps that can process very long documents using the Jamba model

Build Long-Context AI Apps with Jamba

Learn how to use and prompt OpenAI's o1 model for complex reasoning tasks.

Reasoning with o1

Learn how an AI Assistant is built to use and accomplish tasks on computers.