LLM Bootcamp
This project-based bootcamp is designed for beginners to dive practically into the world of Large Language Models (LLMs). Through hands-on building, you will learn how to interact with top-tier AI APIs, master prompt engineering, orchestrate complex workflows using LangChain, and implement Retrieval-Augmented Generation (RAG) to query your own documents. By the end of this course, you will have the skills to build, test, and deploy a fully functional, custom AI web application.
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About This Course
This project-based bootcamp is designed for beginners to dive practically into the world of Large Language Models (LLMs). Through hands-on building, you will learn how to interact with top-tier AI APIs, master prompt engineering, orchestrate complex workflows using LangChain, and implement Retrieval-Augmented Generation (RAG) to query your own documents. By the end of this course, you will have the skills to build, test, and deploy a fully functional, custom AI web application.
Silabus Course
01 Introduction to LLMs & Prompt Engineering Fundamentals
Introduction to LLMs & Prompt Engineering Fundamentals
- What are Large Language Models? (Basic intuition, tokens, and context windows).
- Setting up and interacting with LLM APIs (OpenAI / Gemini API).
- Prompt Engineering 101: Zero-shot and Few-shot prompting.
- Controlling the output: Understanding Temperature, Top-P, and Max Tokens.
- Mini Project: Building a CLI-based Language Translator and Tone Analyzer.
02 Advanced Prompting Strategies
Advanced Prompting Strategies
- Chain-of-Thought (CoT) prompting for complex reasoning.
- System prompts and persona assignments.Handling hallucinations and setting guardrails in prompts.
- Output formatting (forcing JSON outputs).
- Mini Project: Creating a structured Data Extraction tool (extracting entities from unstructured text into JSON).
03 Orchestrating LLMs with LangChain
Orchestrating LLMs with LangChain
- Introduction to LangChain: Why use an LLM framework?
- Core components: Models, Prompts, and Output Parsers.
- Building simple LLM Chains.
- Implementing Memory: Giving your LLM conversation history.
- Mini Project: Building a Terminal Chatbot that remembers user context and previous conversations.
04 Text Embeddings & Vector Databases
Text Embeddings & Vector Databases
- Understanding Text Embeddings: How AI reads and measures semantic similarity.
- Document Loading and Text Splitting strategies (Chunking).
- Introduction to Vector Databases.Storing and querying vectors using ChromaDB (Local).
- Mini Project: Building a semantic search engine to find relevant paragraphs within a large text file.
05 Retrieval-Augmented Generation (RAG) Basics
Retrieval-Augmented Generation (RAG) Basics
- The architecture of a RAG system.
- Connecting the Vector Store Retriever to an LLM Chain.
- Crafting the perfect RAG prompt to synthesize retrieved data.
- Mini Project: "Chat with a Document" (A script that answers questions strictly based on a single uploaded PDF).
06 Building LLM Agents & Tool Integration
Building LLM Agents & Tool Integration
- What is an LLM Agent? (Reasoning and acting).
- Giving LLMs access to the outside world (Tools).
- Integrating external APIs (e.g., Wikipedia, Web Search, Calculators).
- Mini Project: Creating a "Research Assistant Agent" that can search the internet to answer current-event questions and summarize the findings.
07 Exploring Open-Source LLMs & Local Execution
Exploring Open-Source LLMs & Local Execution
- Navigating the Hugging Face ecosystem.
- Introduction to Ollama for local LLM inference.
- Running models (like Llama 3 or Mistral) locally on your machine.
- When to use Cloud APIs vs. Local Open-Source models.
- Mini Project: Modifying the previous RAG pipeline to run 100% locally and offline.
08 Building User Interfaces & Deployment
Building User Interfaces & Deployment
- Introduction to Streamlit for rapid web app development.
- Connecting LangChain logic and session state to a Streamlit UI.
- Designing an intuitive chat interface.
- Basic deployment concepts (e.g., Streamlit Community Cloud).
- Mini Project: Wrapping the terminal chatbot into a responsive web application.
Capstone Project
Domain-Specific AI Knowledge Assistant
For the final project, students will build an interactive, end-to-end web application that serves as a specialized virtual assistant for a specific domain of their choice (e.g., coding documentation, HR manuals, cooking recipes, or academic papers). Using a complete RAG architecture, the application will bypass general LLM knowledge to answer user queries strictly based on the custom documents uploaded to its database.
Why Choose Corporate Training?
Training programs tailored to your team and organization's needs
Team Discounts
Get special pricing for group registrations. The more participants, the bigger the discount.
Custom Curriculum
Training materials can be tailored to your team's specific needs and company projects.
Flexible Schedule
Choose training times that suit your team: weekday, weekend, or special sessions at your office.
Official Certificate
All participants receive a professional certificate upon completion.
Post-Training Support
Get free consultation access for 30 days after training to ensure successful implementation.
Real Projects
Participants will work on real-world projects that can be immediately applied in their work environment.
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Frequently Asked Questions
Find answers to common questions about our training programs
Yes, we provide online (remote), offline (at your office, for corporate only), or hybrid training options based on your team's needs. All formats receive the same materials and certificates.
For corporate training, the minimum is 3 participants. However, we also accept individual registrations.
Absolutely. We offer custom curriculum services where materials can be tailored to your technology stack, active projects, and your team's specific needs.
Yes, we offer special group discounts: 10% for 5-9 participants, 15% for 10-14 participants, and 20-30% for 15+ participants from the same company.
Training duration varies depending on the material. For corporate training, schedules can be customized to your team's needs - weekday, weekend, or custom schedules.
Yes, all participants who complete the training will receive an official certificate from Rumah Coding. Digital certificates can be verified online.
Of course. We provide free consultation support for 30 days after training to help with implementation. Participants also get access to our exclusive community and training recording materials.
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