Module 2: Quiz

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Module 2: Code Assistants and Advanced Workflows

📌 Quiz Topic

 Lesson 1: Deep Dive into Advanced Code Assistant Tools

• Key Topics:

  1. Tool-by-Tool Exploration:

     • GitHub Copilot: enterprise features, integration with VSCode/IntelliJ, usage tips.

     • Amazon CodeWhisperer & Tabnine: comparing features, supported languages, cost models.

     • Cursor: real-time collaboration, prompt-based debugging, possible synergy with other tools.

     • V0: rapid project scaffolding, prototype generation.

     • Windsurf (brief overview): main use cases, unique capabilities.

  2. Comparisons & Use Cases:

     • Which assistants excel at refactoring vs. generating new code?

     • Language coverage (Java, Python, Node.js, etc.) vs. niche languages.

  3. Practical Integration:

     • Setting up each tool: config files, environment variables, or IDE plugins.

     • Dealing with cloud vs. local hosting (on-prem solutions).

 Lesson 2: Advanced Techniques - Prompt Engineering, Documentation- First & Vibe Coding

• Key Topics:

  1. Prompt Engineering:

     • Crafting prompts for specific outputs (e.g., test generation, docstrings, refactor suggestions).

     • Techniques to refine or guide the AI’s responses (few-shot examples, specifying code style, etc.).

  2. Combining Multiple Assistants/APIs:

     • Using different tools in parallel or sequentially (Copilot + Tabnine) or incorporating an LLM API (OpenAI, etc.).

     • Potential synergy vs. confusion or repetitive suggestions.

  3. Documentation-First Workflow (Revisited in Depth):

     • Using docs, contracts, or interface definitions to drive code generation.

     • Keeping docs and implementation synced with minimal manual overhead.

  4. Vibe Coding (Revisited in Depth):

     • Emphasis on flow, iterative exploration with minimal upfront planning.

     • Rapid prototypes, creative coding, or “hackathon” scenarios.

 Lesson 3: Monitoring and Logging for Deployed Systems

• Key Topics:

  1. SWE Benchmarks for LLMs and Agents:

     • Assessing code quality (linting, style compliance).

     • Success rates on unit tests or example test suites.

     • Accuracy metrics or correctness checks.

     • Tools like SWE Lancer or internal test harnesses.

  2. Interpreting Results:

     • Understanding false positives or incomplete suggestions.

     • Distinguishing between speed vs. correctness vs. maintainability.

  3. Making Adoption Decisions:

     • Weighing cost, team familiarity, ecosystem integration, and performance.

     • Potential fallback or layering strategy (e.g., default to one assistant, then cross-check with another)