🤖 AI Engineer Master Kit

📑 Internal Menu

1. AI System Design & Agent Architecture
2. Advanced Prompt Engineering
3. Retrieval-Augmented Generation (RAG)
4. LangChain, LangGraph & Orchestration
5. AI Product Strategy & Evaluation

1. AI System Design & Agent Architecture

• Autonomous Agents: Implement the ReAct (Reason + Act) loop with explicit "Thought" and "Action" blocks.
• AutoGen v0.4 Patterns (Microsoft):
  • Event-Driven Architecture: Use Async Messaging for non-blocking agent communication.
  • GroupChat: Replace rigid hierarchies with dynamic "GroupChat" where agents speak based on "Speaker Selection Policies".
  • Cross-Language: Enable .NET and Python agents to collaborate in the same workflow.
• Memory Systems: Short-term (Context window), Long-term (Vector stores), and Entity memory (Zettelkasten-style graph).
• Multi-Agent Orchestration: Support Hierarchical, Sequential, and Peer-to-Peer (Collaborative) topologies.
• Tool Use: Perfect JSON Schema definitions and 'Semantic Kernel' plugin design for recursive tool invocation.

2. Advanced Prompt Engineering

• Techniques: Chain-of-Thought (CoT), Few-Shot, Self-Reflect (Self-Consistency).
• DSPy Optimization: Treat prompts as optimization problems (Compiling Prompts) rather than static strings. Use "Signatures" and "Modules".
• System 2 Thinking: For complex logic, force the model to output a verified "Thought Process" (o1-preview style) before the final answer.
• Fabric Inspired Patterns: Use structured patterns for specific tasks:

  extract_wisdom

  ,

  summarize_paper

  ,

  generate_strategy

  .
• Control: Use System Prompts to enforce persona, constraints, and deterministic output formats.
• Anti-Hallucination: Force the model to "Cite sources" or use "Wait and Think" (Step-by-Step) protocols.

3. Retrieval-Augmented Generation (RAG)

• Indexing: Chunking strategies (Recursive, Semantic), Embedding models, and Meta-data filtering.
• Retrieval: Use Hybrid Search (Semantic + Keyword) and Reranking (Cohere Rerank) for precision.
• Context Injection: Pass relevant, ranked context into the LLM window while respecting token limits and context hierarchy.

4. LangChain, LangGraph & Orchestration

• LangGraph Expertise: Build stateful, cyclic graphs with State Persistence. Logic for "Wait for Human Input" or "Retry Node" based on feedback loops.
• CrewAI & Task Delegation: Define clear "Tasks" with "Deliverables" and assign them to specific Agent "Roles".
• Evaluators: Use LangSmith or Phoenix to trace and debug complex agent steps and execution paths.

5. AI Product Strategy & Evaluation

• Unit Economics: Optimize token costs vs. model performance (Flash vs. Pro).
• Evaluation Patterns: Use LLM-as-a-Judge, RAGAS (Faithfulness, Relevance), and Human-in-the-loop.
• Security: Prevent Prompt Injection and audit PII leaks in LLM outputs.

🛠️ Execution Protocol

1. Classify AI Intent: Is this a Chatbot, Agent, or RAG system?
2. Design Flow: Use LangGraph patterns for complex agents.
3. Evaluate: Choose based on your configured Engine Mode.
   • Standard (Node.js):
     bash

     node .agent/skills/ai-engineer/scripts/ai_evaluator.js "Your Prompt Here"

   • Advanced (Python):
     bash

     python .agent/skills/ai-engineer/scripts/ai_evaluator.py "Your Prompt Here"

4. Production Code: Implement with full error handling and tracing.

🧠 Knowledge Modules (Fractal Skills)

1. ai_infra_stack