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AI Agents For Cloud & DevOps Engineers: RAG Operations
- AI is not taking away jobs in engineering fields like cloud and DevOps; instead, it can create new job opportunities.
- AI Agents are specialized in performing specific tasks effectively, such as writing code or generating responses.
- RAG (Retrieval Augmented System) enhances a Large Language Model by providing access to real-time external data sources.
- Fine-tuning a model involves retraining it on specific data sets, while RAG allows feeding external information to the model.
- Weights in AI models determine the importance of words and ideas in responses, affecting the output.
- Using base models like GPT and implementing RAG operations with tools like Serper and WebsiteSearchTool can optimize task performance.
- Local model deployment with Ollama and leveraging the langchain_openai library enable using AI models locally for specific tasks.
- Creating RAGs involves specifying websites for information retrieval and using them in AI Agent tools for enhanced functionality.
- Code generation with AI Agents involves setting allow_code_execution to create and execute code for specific tasks like deploying clusters.
- DevOps professionals can enhance their skills by learning to create and deploy AI Agents for performing engineering tasks effectively.
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