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⏱ 2h 30m📚 25 lessons
Go AI Agents with Local LLMs and Ollama
Learn to develop intelligent AI agents and practical web services using Go and local large language models deployed with Ollama.
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About this course
Unlock the potential of artificial intelligence by building your own intelligent agents. This course provides a practical, text-based guide to developing AI agents and integrating them into Go applications using locally hosted Large Language Models.
Upon completing this course, you will be equipped to design, build, and deploy AI agents that leverage local LLMs, creating powerful and context-aware applications entirely within the Go ecosystem.
What you'll learn:
* Understand core concepts of Large Language Models and AI agent architecture.
* Deploy and manage local LLMs efficiently using Ollama.
* Develop robust AI agents in Go for tasks like text processing and interaction.
* Implement conversational interfaces and context management for chatbots.
* Apply fundamental prompt engineering strategies to guide agent behavior.
* Integrate agents into a functional web service using Go.
* Explore basic Retrieval-Augmented Generation (RAG) patterns for enhanced agent knowledge.
This course begins with foundational concepts of LLMs and agent design, progresses through local model deployment with Ollama, and then dives into writing agent logic in Go. You will learn to apply modern prompt engineering techniques and integrate these agents into a complete web service, including basic RAG patterns for more informed responses.
This course is for beginners with some familiarity with Go programming who want to build AI-powered applications. No prior AI or machine learning experience is required.
Start building intelligent Go applications today.
What you'll get
📜Certificate of completion Add it to your LinkedIn profile
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⚡Short & focused 2h 30m of practical content
Certificate of completion
Every course you complete on PickAClass issues a credential like this — original, with its own code, verifiable by URL, and detailed about what was actually demonstrated.