Add guidance documentation for recursive querying and minimal LLM training approaches

[Copilot]

This PR provides comprehensive guidance on implementing recursive querying with different epistemological frameworks and minimal LLM training procedures using the existing RAG-IAC infrastructure.

Analysis Summary

After exploring the repository structure, I found that the existing setup provides an excellent foundation for both requested approaches:

1. Recursive Querying with Different Epistemological Frameworks

Is it good practice? Yes, absolutely. Using notebooks for iterative, framework-based querying is considered a solid approach for several reasons:

• Transparency: Each iteration and framework is documented step-by-step
• Reproducibility: Notebooks capture both code and reasoning processes
• Flexibility: Easy to modify approaches and compare results
• Educational Value: Perfect for understanding how different lenses affect outcomes

Your existing infrastructure supports this well:

• The rag_query.py script provides programmatic access to your RAG system
• Jupyter notebooks allow for iterative experimentation
• The shared Weaviate vector store maintains consistency across queries

Recommended minimal approach:

frameworks = ["analytical", "systems_thinking", "historical", "ethical"]
results = {}

for framework in frameworks:
    prompt = f"Using a {framework} framework, {original_question}"
    results[framework] = query_rag(prompt)
    # Document reasoning and insights in markdown cells

2. Minimal LLM Training Procedure

Is it good practice? Yes, for learning purposes. Your 02_query_eval.ipynb already has foundational structure:

• The create_training_example() function formats Q&A pairs correctly
• Integration with your RAG system for generating training data
• Notebook environment perfect for experimenting with small datasets

Your skeleton is already orthodox:

• Uses standard instruction-input-output format
• Leverages existing RAG context for realistic training data
• Maintains connection to your local Ollama instance for training

For 10 Q&A pairs, this approach is perfectly sufficient and educational. It follows established fine-tuning patterns while keeping complexity minimal.

Existing Foundation

The repository already contains:

• Proper RAG query infrastructure (scripts/rag_query.py)
• Training data preparation functions (notebooks/02_query_eval.ipynb)
• Multi-interface architecture supporting programmatic and interactive use
• Docker-based reproducible environment

Both approaches you're considering align well with the repository's design philosophy of "minimal, scalable, and educational."

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