This course is designed specifically for Go developers who want to break into the world of generative AI without switching tech stacks. You’ll learn to build real-world AI applications using Go-native tools, including the powerful LangChainGo framework, and integrate with leading vector databases like PostgreSQL (pgvector), Pinecone, and Redis.
You’ll start with the fundamentals of generative AI, prompt engineering, and platforms such as OpenAI, Cohere, and Hugging Face that offer foundation models. Then, dive deep into working with embeddings and vector search and use Go to implement retrieval-augmented generation (RAG) pipelines. You’ll also build complete chat applications that can search and summarize document content—all powered by Go.
This course will help you integrate scalable, high-performance AI features into your Go applications, whether you’re a backend engineer or system developer.
This course is designed specifically for Go developers who want to break into the world of generative AI without switching tech ...Show More
WHAT YOU'LL LEARN
An understanding of the fundamentals of generative AI and large language models
The ability to write prompts and interact with LLMs
Hands-on experience using the LangChainGo framework for chaining, embeddings, and retrievers
An understanding of integrating Go with vector databases (PostgreSQL, pgvector, Pinecone, Redis)
An understanding of the RAG (retrieval-augmented generation) workflows in Go
An understanding of the fundamentals of generative AI and large language models
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TAKEAWAY SKILLS
Content
1.
Introduction
4 Lessons
Explore generative AI, prompt engineering, and practical applications using LangChain.
2.
Getting Started with Foundation Models
6 Lessons
Explore leading AI platforms and their capabilities for developing advanced applications.
3.
Vector Databases
3 Lessons
Explore vector databases and embeddings for enhanced AI application development.
4.
PostgreSQL as a Vector Database
3 Lessons
Explore efficient vector storage and build a personalized movie recommendation service.
5.
Pinecone Vector Database
3 Lessons
Explore Pinecone’s capabilities for building efficient AI-driven recommendation systems.
6.
Redis as a Vector Database
3 Lessons
Explore vector search in Redis and build a movie recommendation service.
7.
LangChain Framework Components
5 Lessons
Explore LangChainGo’s framework for efficient AI application development with LLMs.
8.
Using Vector Stores with LangChain
4 Lessons
Explore the integration of vector stores with LangChain for enhanced AI applications.
9.
Build with LangChain
5 Lessons
Develop chat applications using RAG for enhanced accuracy and user interaction.
Certificate of Completion
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