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Gemini CLI Masterclass

Master practical techniques for AI-powered software development with LLMs.

Why This Masterclass?

The Gemini CLI Masterclass is designed for senior engineers, tech leads, and developers who want to go beyond ChatGPT and harness LLM-powered coding with real-world workflows, tools, and techniques, especially for the challenges of brownfield development.

Large Language Models (LLMs) are the new "secret weapons" of modern software engineering. They can transform how you learn, design, analyze, and modernize codebases — if you know how to use them effectively.

While LLMs are proving highly effective for "vibe coding" and greenfield development, their true strategic impact lies in tackling legacy modernization. They deliver the greatest value when applied to large, complex codebases that resist traditional tooling and refactoring.

Crucially, mastering these techniques directly translates to controlled budgets and efficient resource utilization, preventing wasteful, unchecked, or "infinite loop" queries that can quickly escalate costs.

The masterclass specifically leverages Gemini CLI, Google's powerful command-line interface for interacting with its Gemini LLM. However, the tools and techniques taught in the class, work equally well for Cursor, Claude Code, and other IDE LLM code assistants.

Masterclass At Glance

Part 1: Structured Thinking with LLMs

Most users engage with LLMs in an ad-hoc way, but this masterclass provides a systematic framework to unlock their full potential.

To better understand and categorize the diverse ways users engage with large language models, we propose The Cognitive LLM Prompt Types as a foundational framework. These types represent distinct interaction paradigms that leverage the advanced capabilities of LLMs

The Cognitive LLM Prompt Types are:

  1. Tell me about X. -- "How does a streaming MCP server work? Isn’t it just an HTTP server?"

  2. How do I do X? -- "Give me step-by-step instructions to set up a CI/CD pipeline."

  3. What's wrong with X? -- "Refactor this function for better performance."

  4. What if X happened? -- "What would be the impact if database was not available?"

  5. Interact with X. -- "Simulate a user's journey through this new feature."

  6. Compare X and Y. -- "Should we add MemCached or Redis?"

  7. Transform X into Y. -- "Rewrite this function from JavaScript into Python."

  8. Perceive X as Y. -- "Rewrite this technical documentation for a non-technical audience."

These question types form a clear framework for understanding the diverse ways engineers can leverage LLMs. Together with proven methods to avoid common LLM pitfalls like hallucinations and context overload, they deliver predictable, repeatable results instead of ad-hoc prompting.

Part 2: Retrieval-Augmented Generation — Demystified

Out-of-the-box, Gemini CLI alone isn’t enough for tackling large, complex codebases. Modernizing large, aging codebases remains one of the hardest tasks in software engineering. Fortunately, there are proven techniques for advanced grounding like Retrieval-Augmented Generation that work well when applied correctly with LLMs on large code bases.

In the Gemini CLI Masterclass we will cover:

  • Full-text search grounding: A faster, more intelligent alternative to grep for navigating and understanding complex code.

  • Git history grounding: Find patterns, knowledge, use cases and dependencies embedded within your project's commit history.

  • Vector database grounding: Use vector databases to answer precise questions from your own proprietary data.

  • Knowledge graph grounding: Link structured entities, relationships, and metadata for deeper, interconnected system insights.

We’ll demystify these AI patterns and show how they drive legacy modernization, deep code comprehension, and automated documentation—all powered by Gemini CLI.

Part 3: Integrating Gemini CLI into Your Engineering Ecosystem

Gemini CLI is engineered for extensibility. It's an open-source platform designed for deep integration and customization within your operational environment.

In the Gemini CLI Masterclass we will cover:

  • Connecting to Proprietary Data Sources: Connect Gemini CLI to new categories of internal data sources like Jira, bug trackers, CI/CD pipelines, and intranet wikis. This enables new shell commands that query or interact with these systems directly from your terminal.

  • MCP Server: Host and query specialized internal models or sensitive proprietary data securely using Model Context Protocol (MCP) servers. These servers can expose custom LLM-powered commands that execute within your environment, supporting novel tool types beyond basic file operations.

  • Custom Agent Development: Create new AI agents to automate bespoke workflows or address organization-specific problems. These agents can manifest as dedicated Gemini CLI commands (e.g., gemini migrate-system-x, gemini summarize-design-doc ).

  • Semantic Control with gemini.md: For large organizations, consistent LLM behavior is critical. Manage core semantics, coding styles, contextual information, and tool usage at a project or global level using the .gemini/gemini.md file. This provides version-controlled directives for LLM interactions.

  • Model Tuning and Custom Models: Beyond general-purpose LLMs, fine-tune models on your organization's specific datasets or integrate entirely custom models. This allows for highly specialized and accurate AI capabilities tailored to your unique codebase and domain.

Tailor Gemini CLI to become an indispensable component of your engineering ecosystem.

For organizations grappling with legacy modernization, Large Language Models (LLMs) present a transformative opportunity. The common scenario of aging codebases, often crafted by engineers no longer with the company, combined with the sheer volume of code, makes traditional analysis approaches incredibly time-consuming and often ineffective. This is precisely where LLMs can deliver significant leverage.

Even with imperfect outputs, LLMs consistently outperform human capabilities in terms of speed and scope when it comes to understanding and untangling complex legacy systems. They can rapidly sift through vast amounts of code, identify patterns, and surface insights that would take human engineers an unreasonable amount of time to uncover. This accelerates the modernization process, allowing teams to make informed decisions and progress far more efficiently than ever before.

Why Choose This Masterclass?

Hands-on learning: No slides, No PDFs — only real coding on production-quality open-source codebases. Practical skills: simple tools, clear patterns, and proven methods you can use immediately.

About Instructor

Dr. Pasha Simakov is a software engineer with a career dedicated to building intelligent software systems. Currently at Google (LinkedIn), his work spans conventional software engineering as well as applied AI research in topics like: bulk source code transformation, NLP, expert systems, Ontologies & Domain Specific Languages, online education, Google Assistant technologies, and now focuses on applied LLM best practices. Through Gemini CLI, he aims to share practical, real-world techniques that empower both developers and non-technical professionals to make sense of complex codebases and unlock the potential of agentic LLMs.

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Note: Gemini CLI Masterclass is NOT a Google product. Google LLC did not fund, support, approve or endorse this product.
Note: Gemini CLI is a Google product; references here are for educational and practical AI development purposes.