Tomadora
Building AI Apps with LLMs
AI-generated course for Machine Learning & AI covering: Module 1: The LLM Application Development Stack, Module 2: Mastering Prompt Engineering, Module 3: Interacting with LLMs via APIs and SDKs, Module 4: Retrieval-Augmented Generation (RAG), Module 5: Building Autonomous Agents and Tools, Module 6: Fine-Tuning for Specialized Tasks, Module 7: LLMOps - From Prototype to Production, Module 8: Advanced Topics and Responsible AI
Beginner
30 lessons
859 questions
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What you'll learn
This course is part of the Machine Learning & AI track on Tomadora. It covers 8 progressive modules with 30 bite-sized lessons, totalling 859 interactive questions including flashcards, multiple choice, true/false, typing, matching, and fill-in-the-blank.
Course syllabus
Module 1: The LLM Application Development Stack
Discover the new paradigm of building applications with LLMs. This module introduces the core components, from foundational models (OpenAI, Anthropic, open-source) to application frameworks like LangChain and LlamaIndex.
- Lesson 1: Anatomy of an LLM Application (30 questions)
- Lesson 2: Orchestration with LangChain and LlamaIndex (27 questions)
- Lesson 3: The Role of Vector Databases (29 questions)
- Lesson 4: Deployment and Observability (33 questions)
Module 2: Mastering Prompt Engineering
Learn the art and science of instructing LLMs. Progress from basic zero-shot and few-shot prompting to advanced techniques like Chain-of-Thought (CoT) reasoning and generating structured data outputs (JSON/XML).
- The Fundamentals of Effective Prompting (31 questions)
- Advanced Prompting Patterns and Techniques (29 questions)
- Iterative Prompt Development and Optimization (27 questions)
Module 3: Interacting with LLMs via APIs and SDKs
Get hands-on with code. Learn to integrate LLMs into your applications by making API calls, managing keys, handling streaming responses, and utilizing official SDKs for major model providers.
- Lesson 1: Fundamentals of LLM APIs (27 questions)
- Lesson 2: Using SDKs to Streamline Development (27 questions)
- Lesson 3: Advanced Interaction Patterns (25 questions)
Module 4: Retrieval-Augmented Generation (RAG)
Enable LLMs to use external, up-to-date knowledge. Build a complete RAG pipeline from scratch, covering document chunking, embeddings, vector databases, and retrieval strategies to answer questions from private data.
- Introduction to Retrieval-Augmented Generation (RAG) (24 questions)
- Building the Knowledge Base: Embeddings and Vector Stores (28 questions)
- Implementing and Optimizing the RAG Pipeline (28 questions)
- Evaluating RAG Systems (41 questions)
Module 5: Building Autonomous Agents and Tools
Go beyond simple Q&A by building LLM-powered agents. Learn how to give models access to external tools (e.g., APIs, search engines) and implement reasoning frameworks like ReAct to solve complex, multi-step problems.
- Foundations of Autonomous Agents (32 questions)
- Equipping Agents with Tools (32 questions)
- Agentic Reasoning and Planning Loops (8 questions)
- Advanced: Building Multi-Agent Systems (29 questions)
Module 6: Fine-Tuning for Specialized Tasks
Learn when and how to customize a pre-trained LLM for your specific domain or style. This module covers data preparation, the mechanics of launching a fine-tuning job, and evaluating the resulting model's performance.
- Introduction to Fine-Tuning (28 questions)
- Preparing High-Quality Datasets (27 questions)
- The Fine-Tuning Workflow in Practice (28 questions)
- Evaluating and Deploying Fine-Tuned Models (32 questions)
Module 7: LLMOps - From Prototype to Production
Operationalize your LLM application. Tackle the real-world challenges of deployment, including cost management, latency optimization, prompt versioning, caching strategies, and robust evaluation and monitoring.
- Introduction to LLMOps (28 questions)
- Experiment Tracking and Prompt Engineering at Scale (28 questions)
- Deploying and Scaling LLM-Powered APIs (34 questions)
- Monitoring, Observability, and Continuous Improvement (29 questions)
Module 8: Advanced Topics and Responsible AI
Explore the cutting edge of LLM applications. Delve into multi-modal models (text, image, audio), ensuring application safety and security, and implementing strategies for responsible and ethical AI development.
- Enhancing LLMs with Retrieval-Augmented Generation (RAG) (32 questions)
- Building Autonomous AI Agents (28 questions)
- Foundations of Responsible AI: Fairness, Bias, and Transparency (31 questions)
- AI Safety and Security: From Guardrails to Red Teaming (27 questions)
Frequently asked questions
- What is the Building AI Apps with LLMs course?
- Building AI Apps with LLMs is a beginner course on Tomadora covering 8 modules and 30 lessons. It is designed to be completed in 5-minute bursts during your work breaks, using a Pomodoro-style focus + learn cycle.
- How long does Building AI Apps with LLMs take to finish?
- Each lesson takes about 5 minutes. With 30 lessons, you can finish the course in roughly 3 hours of total learning time, spread across as many breaks as you like.
- Is Building AI Apps with LLMs free?
- Yes. Tomadora is free to download and the entire Machine Learning & AI track — including Building AI Apps with LLMs — is free to learn.
- What level is Building AI Apps with LLMs?
- Building AI Apps with LLMs is rated Beginner. No prior knowledge is required.
- What language is Building AI Apps with LLMs taught in?
- Building AI Apps with LLMs is taught in English.
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