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Prompt Quality Evaluation & Summary

⭐ Prompt Quality Evaluation Standards​

A high quality Prompt should meet:

⭐ 1. Clarity​

  • ⭐ Clearly define role (You are a...)
  • ⭐ Clear task (Requirement:...)
  • Specify output format (Please generate...)

⭐ 2. Context Completeness​

  • Provide sufficient background information
  • ⭐ Include relevant code snippets, error logs, business rules
  • Explain tech stack and environment

3. Verifiability​

  • Output results can be verified by clear standards
  • Include quality judgment standards (e.g. "Code must conform to DDD principles")

4. Reusability​

  • Can be used as template for similar scenarios
  • ⭐ Parameterized design (Use [Placeholders] to mark replaceable parts)

5. Clear Constraints​

  • Clearly explain tech stack, standards, limitations
  • ⭐ Explain what not to do (Counter examples)

Scoring Method (5 Point Scale)​

  • 5 Points: Meets all 5 standards, can be used directly in production
  • 4 Points: Meets 4 standards, usable with slight adjustment
  • 3 Points: Meets 3 standards, needs supplementary context or constraints
  • 2 Points and below: Needs rewrite

Skill Combination Summary​

⭐ Left Hand Python (AI Logic)​

  • ⭐ Write LangGraph State Graph
  • ⭐ Advanced RAG Strategy (Parent-Child, HyDE)
  • FastAPI Interface Encapsulation
  • Fine-tuning Scripts (LoRA/QLoRA)

Right Hand Java (Engineering Base)​

  • High Performance Gateway (Spring Cloud Gateway)
  • Business System Integration (Feign Client)
  • Enterprise Authentication (Spring Security)
  • ⭐ Consume JSON data produced by Python (OpenAPI Generate DTO)

⭐ Brain (Architecture Thinking)​

  • Design Dify Workflow
  • ⭐ Router Distribution Strategy
  • ⭐ System Fault Tolerance & Evaluation System
  • ⭐ Heterogeneous System Decoupling (Sidecar Pattern, MQ)

⭐ 2026 Interview Crash List​

⭐ Must-Ask Technical Points​

  1. ⭐ Dify vs LangChain vs Handwritten Code:

    • Dify: 80% general scenarios, fast delivery, but weak customization
    • LangChain: Code level control, but too complex
    • ⭐ LangGraph: Complex decision logic, state graph programming
    • Handwritten Code: Fully customized, but high development cost

  2. ⭐ RAG Advanced:

    • Question: How to solve "Cannot retrieve"?
    • ⭐ Answer: Parent-Child Indexing (Precise Retrieval + Context Retention) or HyDE (Hypothetical Document Embeddings)

  3. ⭐ Structured Output:

    • Question: How does Java business system stably parse AI response?
    • ⭐ Answer: Python side uses Instructor + Pydantic for rigid constraints, Java side uses OpenAPI to generate DTO

  4. Agent Principle:

    • ⭐ ReAct Mode: Reasoning + Acting Loop
    • LangGraph how to solve infinite loop: Set max_iterations and recursion_limit

  5. ⭐ Deployment Architecture:

    • Development Environment: Ollama (5-10 QPS)
    • ⭐ Production Environment: vLLM (100+ QPS, PagedAttention optimizations VRAM)

⭐ Killer Project Experience​

  1. ⭐ "Hybrid Architecture RAG System":

    • Based on Dify for management backend
    • ⭐ Utilize Python + Milvus to implement Parent-Child Indexing Strategy that Dify cannot do
    • Use Golden Dataset (20 QA pairs) for testing, recall rate improved from 0.60 to 0.78 (Relative improvement 30%)

  2. ⭐ "Enterprise Router Agent":

    • ⭐ Implement intelligent distribution based on LangGraph (Milvus/Neo4j/Google 3-way routing)
    • ⭐ Decouple with Java main system via FastAPI + RabbitMQ
    • Implement asynchronous inference under high concurrency (Support 100+ QPS)
    • Establish complete audit log system (Trace complete conversation via session_id)

Core Philosophy​

Your Java/React Experience determines how Stable you can build the system (Complex Architecture Capability), while your AI Tool Driving Capability determines how Fast you can run (Development Efficiency).

Adding these two together makes a true Full Stack AI Engineer.

Capability Evolution Path​

⭐ Critical Success Factors​

  1. ⭐ Architecture Patterns Priority over Tool Learning: Understand Sidecar Pattern, Async Decoupling, Structured Output and other architecture patterns.
  2. Extensions over Deep Dives: Treat platforms like Dify as black boxes, focus on extension and integration.
  3. ⭐ Evaluation Shift Left: Establish Golden Dataset and Automated Evaluation starting from Level 2.
  4. Observability: Establish complete Agent behavior tracking and quality monitoring system.
  5. ⭐ Dual Track Parallel: Master both rapid delivery tools (Dify) and deep customization capabilities (LangGraph).

Return to Overview: AI Prompt Engineering Roadmap - Overview