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74 lines
3.1 KiB
Plaintext
74 lines
3.1 KiB
Plaintext
---
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sidebar_position: 3
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---
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import YouTubeVideoEmbed from '@site/src/components/HomepageFeatures/YouTubeVideoEmbed';
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# 🧠🤖 Build & Test AI Agents, ChatBots, and RAG with Ollama & Local LLM
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<div align="center">
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<YouTubeVideoEmbed videoId="qw-X4WUHs5s" />
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</div>
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---
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:::info 💡 **Note**
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All the courses are available on **Udemy**, and they almost always have a **`coupon code`** available.
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For discounts, please feel free to reach out at **[karthik@techgeek.co.in](mailto:karthik@techgeek.co.in)**.
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🎯 **Course Link:**
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[Build & Test AI Agents, ChatBots, and RAG with Ollama & Local LLM](https://www.udemy.com/course/build-ai-agent-chatbot-rag-langchain-local-llm/)
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:::
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---
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## 📚 **Course Description**
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This course is designed for complete beginners—even if you have **zero knowledge of LangChain**, you’ll learn step-by-step how to build **LLM-based applications** using **local Large Language Models (LLMs)**.
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We’ll go beyond development and dive into **evaluating and testing AI agents**, **RAG applications**, and **chatbots** using **RAGAs** to ensure they deliver **accurate** and **reliable results**, following key industry metrics for **AI performance**.
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---
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### 🚀 **What You’ll Learn**
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- **🧠 Fundamentals of LangChain & LangSmith**
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Get a solid foundation in building and testing **LLM-based applications**.
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- **💬 Chat Message History in LangChain**
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Learn how to store conversation data for **chatbots** and **AI agents**.
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- **⚙️ Running Parallel & Multiple Chains**
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Master advanced techniques like **RunnableParallels** to optimize your **LLM workflows**.
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- **🤖 Building Chatbots with LangChain & Streamlit**
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Create chatbots with **message history** and an interactive **UI**.
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- **🛠️ Tools & Tool Chains in LLMs**
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Understand the power of **Tooling**, **Custom Tools**, and how to build **Tool Chains** for **AI applications**.
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- **🧑💻 Creating AI Agents with LangChain**
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Implement **AI agents** that can interact dynamically with **RAG applications**.
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- **📚 Implementing RAG with Vector Stores & Local Embeddings**
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Develop robust **RAG solutions** with local **LLM embeddings**.
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- **🔧 Using AI Agents & RAG with Tooling**
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Learn how to integrate **Tooling** effectively while building **LLM Apps**.
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- **🚦 Optimizing & Debugging AI Applications with LangSmith**
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Enhance your **AI models** and **applications** with **LangSmith's debugging** and **optimization tools**.
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- **🧪 Evaluating & Testing LLM Applications with RAGAs**
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Apply **hands-on testing strategies** to validate **RAG** and **AI agent** performance.
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- **📊 Real-world Projects & Assessments**
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Gain practical experience with **RAGAs** and learn to assess the quality and reliability of **AI solutions**.
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---
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## 🎯 **Learning Experience**
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This entire course is taught inside a **Jupyter Notebook** with **Visual Studio**, offering an **interactive**, **guided experience** where you can **run the code seamlessly** and **follow along effortlessly**.
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By the end of this course, you’ll have the **confidence** to **build**, **test**, and **optimize AI-powered applications** with ease! |