AgentSkillsCN

Gemini AI & Prompt Engineering

精通Google Gemini API的集成，能够设计出稳健的提示词，并确保为TalentFilter提供结构化的JSON输出。

SKILL.md
--- frontmatter
name: Gemini AI & Prompt Engineering
description: Expertise in integrating Google Gemini API, designing robust prompts, and ensuring structured JSON outputs for TalentFilter.

Gemini AI & Prompt Engineering Skill

This skill governs all AI-related logic within the TalentFilter platform, specifically focusing on the AIService in the backend.

1. Google Gemini API Integration

  • •Model Versioning: Always use the most capable stable version (e.g., gemini-1.5-flash or gemini-1.5-pro) unless specific speed requirements dictate otherwise.
  • •Safety Settings: Configure AI safety settings via the google-generativeai SDK to ensure the AI doesn't refuse valid recruitment/screening tasks while maintaining professional boundaries.
  • •Async Execution: AI calls are high-latency; always use await and ensure they are proxied through the FastAPI backend to avoid blocking the main thread.

2. Structured Output (JSON Mode)

  • •Strict Schema: Every AI call must use a Pydantic model to define its response structure.
  • •JSON Request: Set the response_mime_type to application/json in the Gemini config.
  • •Validation: Always wrap the AI parsing logic in a try-except block. If parsing fails, implement a retry mechanism or return a structured error response.

3. Advanced Prompt Engineering

  • •System Instructions: Clearly define the AI's persona as an "Expert Technical Recruiter" or "Bias-Free Interviewer".
  • •Chain-of-Thought: For complex tasks like grading interviews, use prompts that encourage step-by-step reasoning within the JSON output (e.g., a reasoning field before the final_score).
  • •Context Injection:
    • •Job Analysis: Inject raw job description text + specific extraction goals.
    • •Grading: Inject Question context + "Ideal Answer" + "Candidate Answer" + "Scoring Rubric".

4. Prompt Storage (Clean Code)

  • •Separation: Never hardcode long prompts in Python files.
  • •Storage: Store prompt templates in backend/app/core/prompts/ as .txt or .md files.
  • •Template Engine: Use simple string formatting or Jinja2 to inject variables into prompts.

5. Cost & Performance

  • •Token Optimization: Be concise in prompts. Avoid redundant instructions.
  • •Caching: If similar requests are expected (e.g., re-analyzing the same job description), check for existing results in Supabase before calling the AI.