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

Learn about Anthropic’s family of models, its approach to AI research, and the best way to prompt it, including multi-modal use cases. Learn about Anthropic’s family of models, its approach to AI research, and the best way to prompt it, including multi-modal use cases.Learn effective prompting techniques, implement prompt caching to reduce costs and latency, and build AI applications that can call tools. Learn effective prompting techniques, implement prompt caching to reduce costs and latency, and build AI applications that can call tools.See how you can combine multimodal capabilities, agentic workflows, and tool use to build an AI assistant that can navigate and interact with computer interfaces, executing tasks like web searches. See how you can combine multimodal capabilities, agentic workflows, and tool use to build an AI assistant that can navigate and interact with computer interfaces, executing tasks like web searches._Building Towards Computer Use with Anthropic introduces an innovative capability from Anthropic that enables models to interact with and navigate computer interfaces.Taught by Colt Steele, Anthropic’s Head of Curriculum, this course covers Anthropic’s family of models and the building blocks that lead to the amazing new application – Computer Use. Computer Use utilizes the capabilities of the latest models including image reasoning and tool use to enable an LLM-based agent to use a computer. Like a human user, the model processes an image of the screen, analyzes it to understand what’s going on, and navigates the computer by issuing mouse clicks and generating keyboard strokes to get things done.In this course, you’ll learn the features that lead up to computer use from working with the Anthropic’s API, to multimodal prompting, prompt caching, and tool use, ending in a demo that combines these features to build an AI assistant that uses a computer.In detail, you’ll:Learn Anthropic’s approach to AI research, principles of AI safety, alignment, and interpretability while understanding the key differences between its models.Make API requests to Claude, format messages for better responses, and control API parameters like system prompts, temperature, and max tokens for optimal responses.Write multi-modal prompts that combine text and image content blocks and build with streaming responses.Learn effective prompting techniques such as using prompt templates, structuring prompts in XML, and providing examples to get consistent high-quality responses.Learn to implement prompt caching and see how it can reduce costs and latency.Understand tool-use workflows and build a chatbot that can call different tools in response to users’ queries.See all these concepts come together in a demo that uses Anthropic Computer Use to achieve a task on a computer.Start utilizing Anthropic’s family of models to build towards Computer Use applications._Anyone who has basic Python knowledge, wants to learn how to use all of the features of the Anthropic family of models, and understand the capabilities of computer use applications. 

Building toward Computer Use with Anthropic

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

Learn LangChain directly from the creator of the framework, Harrison Chase Learn LangChain directly from the creator of the framework, Harrison ChaseApply LLMs to your proprietary data to build personal assistants and specialized chatbots Apply LLMs to your proprietary data to build personal assistants and specialized chatbotsUse agents, chained calls, and memories to expand your use of LLMs Use agents, chained calls, and memories to expand your use of LLMs_In LangChain for LLM Application Development, you will gain essential skills in expanding the use cases and capabilities of language models in application development using the LangChain framework.In this course you will learn and get experience with the following topics:Models, Prompts and Parsers: calling LLMs, providing prompts and parsing the responseMemories for LLMs: memories to store conversations and manage limited context spaceChains: creating sequences of operationsQuestion Answering over Documents: apply LLMs to your proprietary data and use case requirementsAgents: explore the powerful emerging development of LLM as reasoning agents.This one-hour course, instructed by the creator of LangChain Harrison Chase as well as Andrew Ng will vastly expand the possibilities for leveraging powerful language models, where you can now create incredibly robust applications in a matter of hours._LangChain for LLM Application Development is a beginner-friendly course. Basic Python knowledge will help you get the most out of this course.

LangChain for LLM Application Development

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.

Efficiently build multi-step systems using large language models.  Efficiently build multi-step systems using large language models.Learn to split complex tasks into a pipeline of subtasks using multistage prompts.  Learn to split complex tasks into a pipeline of subtasks using multistage prompts.Evaluate your LLM inputs and outputs for safety, accuracy, and relevance. Evaluate your LLM inputs and outputs for safety, accuracy, and relevance._In Building Systems with the ChatGPT API, you will learn how to automate complex workflows using chain calls to a large language model. Unlock new development capabilities and improve your efficiency in this brand new short course.You’ll build:Chains of prompts that interact with the completions of prior prompts.Systems where Python code interacts with both completions and new prompts.A customer service chatbot using all the techniques from this course.You’ll learn how to apply these skills to practical scenarios, including classifying user queries to a chat agent’s response, evaluating user queries for safety, and processing tasks for chain-of-thought, multi-step reasoning. This one-hour course, taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI), builds on the lessons taught in the popular ChatGPT Prompt Engineering for Developers, though it is not a prerequisite. Hands-on examples make each concept easy to understand. Built-in Jupyter notebooks allow you to seamlessly experiment with the code and prompts presented in the course._Building Systems With The ChatGPT API is beginner-friendly. You only need a basic understanding of Python to be able to complete the course. This course is also suitable for intermediate or advanced machine learning engineers who want to learn cutting-edge prompt engineering skills for LLMs. 

Building Systems with the ChatGPT API

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

Learn to evaluate programs utilizing LLMs as well as generative image models using platform-independent tools Learn to evaluate programs utilizing LLMs as well as generative image models using platform-independent toolsInstrument a training notebook, and add tracking, versioning, and logging Instrument a training notebook, and add tracking, versioning, and loggingImplement monitoring and tracing of LLMs over time in complex interactions Implement monitoring and tracing of LLMs over time in complex interactions_Machine learning and AI projects require managing diverse data sources, vast data volumes, model and parameter development, and conducting numerous test and evaluation experiments. Overseeing and tracking these aspects of a program can quickly become an overwhelming task.This course will introduce you to Machine Learning Operations tools that manage this workload. You will learn to use the Weights & Biases platform which makes it easy to track your experiments, run and version your data, and collaborate with your team.This course will teach you to:Instrument a Jupyter notebookManage hyperparameter configLog run metricsCollect artifacts for dataset and model versioningLog experiment resultsTrace prompts and responses to LLMs over time in complex interactionsWhen you complete this course, you will have a systematic workflow at your disposal to boost your productivity and accelerate your journey toward breakthrough results._Anyone who has familiarity with Python and PyTorch or similar framework and an interest in managing, versioning, and debugging their machine learning workflow.

Evaluating and Debugging Generative AI

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

Learn about the most recent advancements in LLM APIs. Learn about the most recent advancements in LLM APIs.Use LangChain Expression Language (LCEL), a new syntax to compose and customize chains and agents faster. Use LangChain Expression Language (LCEL), a new syntax to compose and customize chains and agents faster.Apply these new capabilities by building up a conversational agent. Apply these new capabilities by building up a conversational agent._The landscape of LLMs and the libraries that support them has evolved rapidly in recent months. This course is designed to keep you ahead of these changes. You’ll explore new advancements like ChatGPT’s function calling capability, and build a conversational agent using a new syntax called LangChain Expression Language (LCEL) for tasks like tagging, extraction, tool selection, and routing.After taking this course, you’ll know how to: Generate structured output, including function calls, using LLMs;Use LCEL, which simplifies the customization of chains and agents, to build applications;Apply function calling to tasks like tagging and data extraction;Understand tool selection and routing using LangChain tools and LLM function calling – and much more.Start applying these new capabilities to build and improve your applications today._Anyone who’s interested in learning about the latest tools to build LLM-based applications. Basic Python knowledge and a familiarity with writing prompts for LLMs is recommended.

Functions, Tools and Agents with LangChain

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.

Learn best practices specific to prompting Llama 2 & 3 models. Learn best practices specific to prompting Llama 2 & 3 models.Interact with Meta Llama 2 Chat, Code Llama, and Llama Guard models. Interact with Meta Llama 2 Chat, Code Llama, and Llama Guard models.See how you can build safe, responsible AI applications using the Llama Guard model. See how you can build safe, responsible AI applications using the Llama Guard model._Open up your prompt engineering to the Llama 2 & 3 collection of models! Learn best practices for prompting and building applications with these powerful open commercial license models.Interact with the Llama 2 and Llama 3 models with a simple API call, and explore the differences in output between models for a variety of tasks.What you’ll do: Learn best practices for prompting and selecting among the Llama 2 & 3 models by using them as a personal assistant to help you complete day-to-day tasks.Experiment with advanced prompt engineering techniques, like few-shot prompting to get Llama 2 to classify the sentiment of text messages, and chain-of-thought prompting to solve logic problems.Treat Code Llama as a pair programming partner to both learn to write and improve code.Promote safe and responsible use of LLMs by having Llama Guard check user prompts and model responses for harmful content.Llama 2 and Llama 3 models and model weights are free to download, including quantized model versions that can run on your local machine. Join a thriving community of open source developers that is building applications powered by Llama 2 and Llama 3._Anyone who is interested in learning prompt engineering and wants to try out Meta Llama 2 and Llama 3 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.

Find and filter open source models on Hugging Face Hub based on task, rankings, and memory requirements. Find and filter open source models on Hugging Face Hub based on task, rankings, and memory requirements.Write just a few lines of code using the transformers library to perform text, audio, image, and multimodal tasks. Write just a few lines of code using the transformers library to perform text, audio, image, and multimodal tasks.Easily share your AI apps with a user-friendly interface or via API and run them on the cloud using Gradio and Hugging Face Spaces. Easily share your AI apps with a user-friendly interface or via API and run them on the cloud using Gradio and Hugging Face Spaces._The availability of models and their weights for anyone to download enables a broader range of developers to innovate and create.In this course, you’ll select open source models from Hugging Face Hub to perform NLP, audio, image and multimodal tasks using the Hugging Face transformers library. Easily package your code into a user-friendly app that you can run on the cloud using Gradio and Hugging Face Spaces.You will:Use the transformers library to turn a small language model into a chatbot capable of multi-turn conversations to answer follow-up questions.Translate between languages, summarize documents, and measure the similarity between two pieces of text, which can be used for search and retrieval.Convert audio to text with Automatic Speech Recognition (ASR), and convert text to audio using Text to Speech (TTS).Perform zero-shot audio classification, to classify audio without fine-tuning the model.Generate an audio narration describing an image by combining object detection and text-to-speech models.  Identify objects or regions in an image by prompting a zero-shot image segmentation model with points to identify the object that you want to select.Implement visual question answering, image search, image captioning and other multimodal tasks.Share your AI app using Gradio and Hugging Face Spaces to run your applications in a user-friendly interface on the cloud or as an API. The course will provide you with the building blocks that you can combine into a pipeline to build your AI-enabled applications!_Anyone who wants to get started building AI applications quickly and easily using open source models.

Open Source Models with Hugging Face

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

Learn to identify and evaluate vulnerabilities in large language model (LLM) applications. Learn to identify and evaluate vulnerabilities in large language model (LLM) applications. Apply red teaming techniques from cybersecurity to ensure the safety and reliability of your LLM application. Use an open source library from Giskard to help automate LLM red-teaming methods._Learn how to test and find vulnerabilities in your LLM applications to make them safer. In this course, you’ll attack various chatbot applications using prompt injections to see how the system reacts and understand security failures. LLM failures can lead to legal liability, reputational damage, and costly service disruptions. This course helps you mitigate these risks proactively. Learn industry-proven red teaming techniques to proactively test, attack, and improve the robustness of your LLM applications.In this course:Explore the nuances of LLM performance evaluation, and understand the differences between benchmarking foundation models and testing LLM applications.Get an overview of fundamental LLM application vulnerabilities and how they affect real-world deployments.Gain hands-on experience with both manual and automated LLM red-teaming methods.See a full demonstration of red-teaming assessment, and apply the concepts and techniques covered throughout the course.After completing this course, you will have a fundamental understanding of how to experiment with LLM vulnerability identification and evaluation on your own applications._Red Teaming LLM Applications is a beginner-friendly course. Basic Python knowledge is recommended to get the most out of this course.

Red Teaming LLM Applications

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.

Explore Mistral's three open source models (Mistral 7B, Mixtral 8x7B, and the latest Mixtral 8x22B), and three commercial models (small, medium, and large), which Mistral provides access to via web interface and API calls. Explore Mistral’s three open source models (Mistral 7B, Mixtral 8x7B, and the latest Mixtral 8x22B), and three commercial models (small, medium, and large), which Mistral provides access to via web interface and API calls.Leverage Mistral's JSON mode to generate LLM responses in a structured JSON format, enabling integration of LLM outputs into larger software applications. Leverage Mistral’s JSON mode to generate LLM responses in a structured JSON format, enabling integration of LLM outputs into larger software applications.Use Mistral's API to call user-defined Python functions for tasks like web searches or retrieving text from databases, enhancing the LLM's ability to find relevant information to answer user queries. Use Mistral’s API to call user-defined Python functions for tasks like web searches or retrieving text from databases, enhancing the LLM’s ability to find relevant information to answer user queries._In this course, you’ll access Mistral AI’s collection of open source and commercial models, including the Mixtral 8x7B model, and the latest Mixtral 8x22B. You’ll learn about selecting the right model for your use case, and get hands-on with features like effective prompting techniques, function calling, JSON mode, and Retrieval Augmented Generation (RAG).In detail:Access and prompt Mistral models via API calls for tasks, and decide whether your task is either a simple task (classification), medium (email writing), or advanced (coding) level of complexity, and consider speed requirements to choose an appropriate model.Learn to use Mistral’s native function calling, in which you give an LLM tools it can call as needed to perform tasks that are better performed by traditional code, such as querying a database for numerical data. Build a basic RAG system from scratch with similarity search, properly chunk data, create embeddings, and implement this tool as a function in your chat system.Build a chat interface to interact with the Mistral models and ask questions about a document that you upload.By the end of this course, you’ll be equipped to leverage Mistral AI’s leading open source and commercial models._Getting Started with Mistral is a beginner-friendly course and it’s suitable for anyone who wants to learn about and use Mistral AI’s collection of advanced open source and commercial LLMs. If you have taken ChatGPT Prompt Engineering for Developers or Prompt Engineering with Llama 2, this is a great next step!

Getting Started with Mistral

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

Prompt vision models with text, coordinates, and bounding boxes, and tune hyper-parameters like guidance scale, strength, and number of inference steps. Prompt vision models with text, coordinates, and bounding boxes, and tune hyper-parameters like guidance scale, strength, and number of inference steps.Replace parts of an image with generated content with in-painting, a technique that combines object detection, image segmentation, and image generation. Replace parts of an image with generated content with in-painting, a technique that combines object detection, image segmentation, and image generation.Fine-tune a diffusion model to have even more control over your image generation to create specific images, including your own, rather than generically generated images. Fine-tune a diffusion model to have even more control over your image generation to create specific images, including your own, rather than generically generated images._Prompt engineering is used not only in text models but also in vision models. Depending on the vision model, they may use text prompts, but can also work with pixel coordinates, bounding boxes, or segmentation masks.In this course, you’ll learn to prompt different vision models like Meta’s Segment Anything Model (SAM), a universal image segmentation model, OWL-ViT, a zero-shot object detection model, and Stable Diffusion 2.0, a widely used diffusion model. You’ll also use a fine-tuning technique called DreamBooth to tune a diffusion model to associate a text label with an object of your preference.In detail, you’ll explore:Image Generation: Prompt with text and by adjusting hyperparameters like strength, guidance scale, and number of inference steps. Image Segmentation: Prompt with positive or negative coordinates, and with bounding box coordinates.Object detection: Prompt with natural language to produce a bounding box to isolate specific objects within images.In-painting: Combine the above techniques to replace objects within an image with generated content.Personalization with Fine-tuning: Generate custom images based on pictures of people or places that you provide, using a fine-tuning technique called DreamBooth.Iterating and Experiment Tracking: Prompting and hyperparameter tuning are iterative processes, and therefore experiment tracking can help to identify the most effective combinations. This course will use Comet, a library to track experiments and optimize visual prompt engineering workflows._Prompt Engineering for Vision Models is a hands-on course that helps you get started with prompting vision models. Python experience is recommended. 

Prompt Engineering for Vision Models

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

Learn Python programming fundamentals and how to integrate AI tools for data manipulation, analysis, and visualization. Learn Python programming fundamentals and how to integrate AI tools for data manipulation, analysis, and visualization.Discover how Python can be applied in various domains such as business, marketing, and journalism to solve real-world problems and enhance efficiency through practical applications. Discover how Python can be applied in various domains such as business, marketing, and journalism to solve real-world problems and enhance efficiency through practical applications.Leverage AI assistants to debug code, explain concepts, and enhance your learning, mirroring real-world software development practices. Leverage AI assistants to debug code, explain concepts, and enhance your learning, mirroring real-world software development practices._AI Python for Beginners is designed to help you leverage the power of Python programming, even if your goal isn’t to become a software developer or AI engineer. This four-part course teaches you to code practical AI applications from day one, whether you’re an experienced programmer, or writing “Hello, World!” for the first time. You’ll learn with support from an AI chatbot that can provide you with immediate feedback, answer your questions, quickly identify and work through bugs, and keep you on track while learning new skills. You’ll gain a foundational understanding of Python while using it to build AI-powered tools like custom recipe generators, smart to-do lists, and vacation planners, learning essential programming concepts such as variables, functions, loops, and data structures along the way. As you progress, you’ll work with your own data, extracting valuable insights from text files and structured data, including planning a dream vacation by having AI analyze travel blogs and generate personalized itineraries!By the end of this course series, you’ll be able to write Python scripts that interact with large language models, automate tasks, and analyze your own data. You’ll even learn how to extend Python’s capabilities using popular third-party packages for data analysis and visualization, and how to access real-time information through APIs. These are skills that are increasingly valuable across industries from tech and finance to healthcare and creative fields._This course is for anyone curious about AI and programming with Python, from complete beginners learning to code for the first time to professionals seeking to boost productivity and learn how to properly integrate AI into their coding process. Ideal for students, career changers, knowledge workers, lifelong learners, and educators. If traditional coding courses haven’t worked or have felt intimidating, our hands-on, AI-focused approach will help you in your journey.Whether you want to automate repetitive tasks, extract insights from large datasets, or create AI-powered tools to enhance your work or personal projects, this course will give you the skills to get started. By the end, you’ll not only understand Python basics but also know how to leverage powerful data analysis libraries, interact with web APIs, and set up Python on your own computer to continue your learning journey.

AI Python for Beginners

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

Explore the features of the new Llama 3.2 model, from image classification, vision reasoning to tool use. Explore the features of the new Llama 3.2 model, from image classification, vision reasoning to tool use.Learn the details of Llama 3.2 prompting, tokenization, built-in and custom tool calling. Learn the details of Llama 3.2 prompting, tokenization, built-in and custom tool calling.Gain knowledge of the Llama stack, which is a standardized interface for building AI applications. Gain knowledge of the Llama stack, which is a standardized interface for building AI applications._Join our new short course, Introducing Multimodal Llama 3.2, and learn from Amit Sangani, Senior Director of AI Partner Engineering at Meta, to learn all about the latest additions to the Llama models 3.1 and 3.2, from custom tool calling to multimodality and the new Llama stack.Open models are a key building block of AI and a key enabler of AI research. With Meta’s family of open models, anyone can download, customize, fine-tune, or build new applications on top of them, allowing AI innovation. The Llama model family now ranges from 1B model parameters to its 405B foundation model, allowing for diverse use cases and applications.In this course, you’ll learn about the new vision capabilities that Llama 3.2 brings to the Llama family. You’ll learn how to leverage this along with tool-calling, and Llama Stack, which is an open-source orchestration layer for building on top of the Llama family of models.In detail, you’ll: Learn about the new models, how they were trained, their features, and how they fit into the Llama family.Understand how to do multimodal prompting with Llama and work on advanced image reasoning use cases such as understanding errors on a car dashboard, adding up the total of three restaurant receipts, grading written math homework, and many more.Learn different roles—system, user, assistant, ipython—in the Llama 3.1 and 3.2 family and the prompt format that identifies those roles.Understand how Llama uses the tiktoken tokenizer, and how it has expanded to a 128k vocabulary size that improves encoding efficiency and enables support for seven non-English languages.Learn how to prompt Llama to call both built-in and custom tools with examples for web search and solving math equations.Learn about ‘Llama Stack API’, which is a standardized interface for canonical toolchain components like fine-tuning or synthetic data generation to customize Llama models and build agentic applications.Start building exciting applications on Llama!_Anyone who has basic Python knowledge and wants to learn to quickly build on Llama and Llama stack.

Introducing Multimodal Llama 3.2

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

Learn LLM app development by designing and developing a text-based AI game, creating immersive game worlds, characters, and storylines through hierarchical content generation.  Learn LLM app development by designing and developing a text-based AI game, creating immersive game worlds, characters, and storylines through hierarchical content generation. Learn to implement game mechanics using AI to convert text data into structured JSON outputs, enabling features like an inventory detection system. Learn to implement game mechanics using AI to convert text data into structured JSON outputs, enabling features like an inventory detection system.Learn to enforce safety and compliance for AI content creation, and create custom policies using Llama Guard. Learn to enforce safety and compliance for AI content creation, and create custom policies using Llama Guard._Join our new short course, Building an AI-Powered Game, with Together AI and AI Dungeon! Learn from Niki Birkner, Senior Product Manager at Together AI, and Nick Walton, CEO and Co-Founder of Latitude.This course teaches you how to build a LLM-based application by creating your own AI-powered text-based game. You’ll create a world with hierarchical content generation based on a prompt, setting up its core mechanics, and giving it a simple Gradio user interface. By the end of this course, you’ll have a playable text-based game to share with your friends, built from scratch using LLMs. You will be guided through the building blocks of app development using LLMs, including incorporating safeguards using Llama Guard. What you’ll do: Use prompt engineering to create a world with hierarchical content generation so your narrative is consistent and the AI can flesh out your game.Build the first version of your game with a user interface, load the world, and define the core action loop of the game, so you can have a game to play and interact with.Implement safe user-dependent and context-dependent guardrails into your game application with Llama Guard and custom content policy.Learn LLM tool calling by adding story and state components that improve your game’s memory, track inventory, and show progress.  With these techniques, you’ll hone your AI skills by building your own sharable game! _Anyone who has basic Python knowledge and wants to learn how to build with LLMs in a hands-on, fun way!

Building an AI-Powered Game

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

Learn how the Jamba model integrates transformers and the Mamba architecture to efficiently process long contexts while maintaining quality. Learn how the Jamba model integrates transformers and the Mamba architecture to efficiently process long contexts while maintaining quality.Understand the training process for long context models, and the metrics used to evaluate their performance. Understand the training process for long context models, and the metrics used to evaluate their performance.Gain hands-on experience applying Jamba to tasks such as processing large documents, tool-calling, and building large context RAG apps. Gain hands-on experience applying Jamba to tasks such as processing large documents, tool-calling, and building large context RAG apps._Learn to use the Jamba model, a hybrid transformer-Mamba architecture trained to handle long contexts, in this new course, Build Long-Context AI Apps with Jamba, built in partnership with AI21 Labs and taught by Chen Wang and Chen Almagor.The transformer architecture is the foundation of most large language models but it is computationally expensive when handling very long input contexts. There’s an alternative to transformers called Mamba. Mamba is a selective state space model that can process very long contexts with a much lower computational cost. However, researchers found that the pure Mamba architecture underperforms in understanding the context, and the quality of the output can be lower even in tasks as simple as repeating the input in the output of the model.To overcome these challenges, AI21 Labs developed the Jamba model, which combines Mamba’s computational efficiency with the transformer’s attention mechanism to help with the output quality.In this course, you’ll learn about the Jamba architecture, how it works, and how it is trained. You’ll also learn how to prompt Jamba and use it to process long documents and build long-context RAG apps.In detail, you’ll:Learn key information about Jamba’s large context model and its unique, hybrid architecture that allows for high-quality outputs, high throughput, and low memory usage.Learn how Jamba combines transformers with Mamba, a selective state-space model to achieve high performance and quality.Use the AI21SDK, especially its document parameter, with an example of prompting a large over 200k-token annual report.Use Jamba for tool-calling with hands-on examples from calling simple arithmetic functions to a function that returns SEC 10-Q company quarterly report.Learn how the training for long context is done, and the metrics used for evaluating the performance.Create a RAG app using the AI21 Conversational RAG tool and build your own RAG pipeline that uses the Jamba model with LangChain.Start building AI apps that can handle context as long as all of your unread emails from the last 20 years!_Anyone who has basic Python knowledge and wants to learn more about how the Jamba model works and how it is used for building long-context AI apps.