Prompting for Effective LLM Reasoning with Gemini

Explore agentic AI concepts using Gemini models and Google Cloud Platform, covering prerequisites, environment setup, and key features for effective LLM reasoning.

Explains the theory of using roles or personas to control the tone, style, and expertise of an LLM's output.

Learn to create expert business personas for Vertex AI Gemini, guiding the model with role-based prompts to produce domain-specific, high-quality, specialized AI responses.

Explains the conceptual frameworks for Chain-of-Thought (CoT) for guided reasoning and ReAct (Reason+Act) for enabling agents to plan and take actions.

Learn to build iterative, reasoning agents with Chain-of-Thought and ReACT prompting using Vertex AI Gemini to solve complex, multi-step business problems systematically.

Explains the theory of systematically refining prompt instructions by modifying components like Role, Task, Context, Examples, and Output Format.

Learn to refine and optimize AI prompts using Vertex AI Gemini, applying systematic quality metrics like clarity, specificity, completeness, and structure.

Explains the conceptual framework for building multi-step AI workflows by linking the output of one prompt to the input of the next, and the importance of validation.

Learn to implement prompt chaining with Vertex AI Gemini for agentic reasoning: build sequential workflows, manage context, handle errors, and ensure quality in multi-step tasks.

Explains the conceptual framework for building self-improving systems where an agent uses feedback from its own actions to iteratively refine its output.

Learn to implement automated feedback loops for code generation using Vertex AI Gemini, covering self-validation, intelligent retries, quality gates, and production monitoring.