rag_docs/README.md

RAG Documents

This folder contains documents used by the AI chatbot for Retrieval-Augmented Generation (RAG).

Supported File Types

• .txt - Plain text files
• .md - Markdown files

How to Add Documents

1. Place your documentation files in this folder
2. Delete embeddings_cache.json if it exists (to force re-indexing)
3. The chatbot will automatically index new documents on startup
4. Documents are chunked and embedded for semantic search

Document Inventory

Category 1: General LLM/Transformer Knowledge

• what_is_an_llm.md - Neural networks, language models, next-token prediction
• transformer_architecture.md - Layers, encoder/decoder, residual stream
• tokenization_explained.md - Subword tokenization, BPE, token IDs
• embeddings_explained.md - Lookup tables, vector spaces, positional encodings
• attention_mechanism.md - Q/K/V, multi-head attention, intuitive explanations
• mlp_layers_explained.md - Feed-forward networks, knowledge storage, expand-compress
• output_and_prediction.md - Logits, softmax, temperature, greedy vs. sampling
• key_terminology.md - Extended glossary of ML/transformer terms

Category 2: Dashboard Components

• dashboard_overview.md - Layout tour, navigation, typical workflow
• pipeline_stages.md - What each of the 5 pipeline stages shows
• ablation_panel_guide.md - How to use the ablation experiment panel
• attribution_panel_guide.md - How to use the token attribution panel
• beam_search_and_generation.md - Beam search, generation controls
• head_categories_explained.md - Previous-Token, Positional, BoW, Syntactic, Other
• model_selector_guide.md - Choosing models, auto-detection, generation settings

Category 3: Model-Specific Documentation

• gpt2_overview.md - GPT-2 architecture, why it's a good starter, variants
• gpt_neo_overview.md - GPT-Neo architecture, local attention, comparison with GPT-2
• pythia_overview.md - Pythia architecture, RoPE, parallel attn+MLP, interpretability focus
• opt_overview.md - OPT architecture, ReLU activation, comparison with GPT-2
• qwen_overview.md - Qwen2.5 (LLaMA-like) architecture, RMSNorm, SiLU, GQA

Category 4: Guided Experiments (Step-by-Step)

• experiment_first_analysis.md - Your first analysis with GPT-2
• experiment_exploring_attention.md - Reading attention patterns and head categories
• experiment_first_ablation.md - Removing a head and observing the effect
• experiment_token_attribution.md - Measuring token influence with gradients
• experiment_comparing_heads.md - Systematic comparison across head categories
• experiment_beam_search.md - Exploring alternative generation paths

Category 5: Interpretation, Troubleshooting, and Research

• interpreting_ablation_results.md - How to read ablation probability changes
• interpreting_attribution_scores.md - Understanding attribution score values
• interpreting_attention_maps.md - Reading BertViz patterns visually
• troubleshooting_and_faq.md - Common issues and frequently asked questions
• recommended_starting_points.md - Best models, prompts, and experiment order
• mechanistic_interpretability_intro.md - Mech interp research context

Notes

• Large files will be automatically chunked (~500 tokens per chunk)
• Embeddings are cached in embeddings_cache.json for faster subsequent loads
• Delete embeddings_cache.json to force re-indexing of all documents