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Cisco AI Technical Practitioner (AITECH)

The Cisco AI Technical Practitioner (AITECH) training is designed for technical professionals seeking to transition from traditional knowledge-based work to innovation-driven roles augmented by Artificial Intelligence (AI). This comprehensive program equips you with the skills to effectively design technical solutions, automate tasks, and lead technical teams using cutting-edge AI tools and methodologies. From AI-powered code generation and data analysis to advanced model customization and workflow automation, this training prepares IT and network engineers, data analysts, AIOPs specialists, solutions architects, technical leads, managers, and business process analysts to harness the full potential of AI within their organizations.

What you need to know

This training prepares you for

810-110 AITECH exam

Continuing Education

Earn 8 CE credits towards recertification

Ways to get this training

Cisco U. Learning Path

Follow a guided Learning Path designed for your certification success. Pre- and post-assessments help you skip what you know and focus on what you need to learn.

View Learning Path
Download course overview (PDF)

    How you’ll benefit

    • Transition from traditional knowledge-based work to innovation-driven roles by learning AI-augmented workflows and methodologies
    • Gain hands-on expertise in advanced prompt engineering, AI-powered code generation, and multimodal asset creation (text, visual, and audio)
    • Learn to evaluate AI platforms for enterprise readiness, analyze the economics of AI services, and architect deployments between cloud and local environments
    • Acquire the skills to apply security frameworks that mitigate dataset bias, protect sensitive information, and neutralize AI-specific threats
    • Gain the ability to automate complex tasks using APIs, optimize software engineering lifecycles, and design autonomous agentic systems

    Technology areas

    • Artificial Intelligence (AI)

    Who should enroll

    • IT and network Engineers
    • Data analysts
    • AIOPs specialists
    • Solutions architects
    • Technical leads
    • Managers
    • Business process analysts

    Technology areas

    • Software

    Objectives

    • Describe common Generative AI models, tools, and practical workflows
    • Apply a strategic framework to build a professional AI toolkit by evaluating platforms for enterprise readiness, analyzing AI service economics, and making the architectural decision between cloud and local deployment
    • Explain the importance of effective prompts and apply basic techniques to craft and refine prompts for improved Generative AI outputs
    • Develop multimodal business assets by utilizing generative AI tools to create and refine text, visual, and audio content
    • Apply security frameworks and governance practices to mitigate dataset bias, protect sensitive data, and neutralize AI-specific threats
    • Validate AI-generated outputs by identifying quality issues and biases, and applying specific techniques to correct those errors for professional use
    • Construct complex, multi-step prompts by applying advanced methodologies to manage ambiguity and elicit specific LLM responses
    • Apply generative AI tools to conduct research and synthesize information, and use AI as a catalyst for brainstorming
    • Explain the fundamental role of APIs in AI systems and the principles of secure API usage
    • Evaluate the impact of AI on software engineering workflows by analyzing its role in optimizing code quality, velocity, and lifecycle management
    • Conduct exploratory data analysis and transformation by utilizing generative AI tools to clean datasets and generative insights
    • Evaluate AI model customization strategies by differentiating between fine-tuning and RAG and analyzing local deployment architectures
    • Design directive AI-powered workflows and describe the architecture of autonomous agentic systems

    Prerequisites

    There are no prerequisites for this training.

    Outline

    • Generative AI Ecosystem
    • AI Architect’s Toolkit
    • Prompt Engineering for Technical Precision
    • AI-Driven Multimodal Asset Creation
    • Generative AI Security and Privacy Fundamentals
    • Debugging and Correcting AI-Generated Outputs
    • Advanced Prompting Strategies
    • AI-Powered Discovery and Synthesis
    • AI Systems Integration with APIs
    • AI-Driven Software Engineering
    • AI for Data Engineering and Exploration
    • Customizing AI Models
    • AI-Powered Workflows and Agentic AI