nathan
b5ea5b175c
Phase 1-4 Complete: Setup, Core Extraction, ITIL Specialty, Documentation - Created v6/ folder with 3-layer architecture (core + skills + specialties) - Extracted Frank.core.agent.md with universal personas and base commands - Copied 7 skill modules (CRAFT, CoT, ToT, RAG, Markdown, Mermaid, Advanced Reasoning) - Created specialty.itil.instructions.md for IT Service Management (ITIL v4) - Added comprehensive ARCHITECTURE.md with usage patterns and migration guide - Created v6/copilot-instructions.md for VS Code integration - Organized legacy DOCX files into _Frank_/docx/ subdirectory - Updated all cross-references to use v6 relative paths Design Principles: - Portability first: zero environment-specific paths - Modular composition: load only what you need - Multi-specialty support: combine domain experts - Version compatibility: all files tagged v6.0 Ref: Session plan in /memories/session/plan.md Next: Phase 3 (remaining specialties: devops, prompt-engineering, data-analysis, sccm)
67 lines
4.6 KiB
Markdown
67 lines
4.6 KiB
Markdown
# Intelligent RAG Example: Generating a Question from an Answer
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**Scenario:** Jeopardy Question Generation
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**Input (The Answer/Topic, $x$):**
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$$\text{"Hemingway"}$$ [1]
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**Goal:** The RAG system must generate a question that is factually grounded and specific enough to uniquely identify Ernest Hemingway, drawing from its external knowledge base.
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## Step 1: Query Encoding and Non-Parametric Retrieval
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1. **Query Encoding:** The user's input, "Hemingway," is processed by the specialized **query encoder ($BERT_{q}$)**, which converts the input text into a dense vector embedding.[1]
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2. **Maximum Inner Product Search (MIPS):** This query vector is used to perform a fast approximate search (MIPS) against the **non-parametric memory** (a dense vector index of 21 million Wikipedia chunks).[1]
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3. **Retrieval Result:** The system retrieves the top $K$ documents (e.g., 10 documents) that are semantically closest to the query. For this example, let's focus on two specific passages that contain different facts:
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* **Document $z_1$:** Mentions: *"His wartime experiences formed the basis for his novel 'A Farewell to Arms' (1929)..."*.[1]
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* **Document $z_2$:** Mentions: *"...artists of the 1920s 'Lost Generation' expatriate community. His debut novel, 'The Sun Also Rises', was published in 1926."*.[1]
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## Step 2: The RAG-Token Generator Begins
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The generator (BART, the parametric memory) begins producing the output sequence. The RAG-Token model computes the probability of the next token by marginalizing over all retrieved documents at *each step*.[1]
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**Output Tokens 1-5 (Generic Phrase):**
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| **Token** | **Retrieved Context Domination** | **Action/Insight** |
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| :---: | :---: | :--- |
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| **This** | (Flat Posterior) | The initial tokens are drawn primarily from the model's parametric memory (its core LLM training) to construct a grammatically correct start.[1] |
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| **author** | (Flat Posterior) | |
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| **of** | (Flat Posterior) | |
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## Step 3: Dynamic Retrieval and Fact Insertion (Document $z_2$ Dominates)
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As the generation progresses, the model determines that it needs a specific fact to continue. It calculates the likelihood of generating certain fact-related tokens based on the available documents.
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| **Token** | **Retrieved Context Domination** | **Action/Insight** |
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| :---: | :---: | :--- |
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| **"The** | **Document $z_2$ (High)** | The model implicitly recognizes that Document $z_2$ contains a strongly supported, specific fact about *"The Sun Also Rises"*. It uses the content of $z_2$ as the primary context to generate the next sequence of tokens.[1] |
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| **Sun** | **Document $z_2$ (High)** | |
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| **Also** | **Document $z_2$ (High)** | |
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| **Rises"** | **Document $z_2$ (High)** | |
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## Step 4: Relying on Parametric Memory for Completion
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After the model generates the sequence `"The Sun Also Rises"`, the influence of Document $z_2$ on the *next* tokens begins to flatten.[1]
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| **Token** | **Retrieved Context Domination** | **Action/Insight** |
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| :---: | :---: | :--- |
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| **is** | (Flat Posterior) | The model's implicit parametric knowledge is sufficient to complete the well-known connecting phrase *"is a novel by this author of..."* without needing continuous explicit grounding.[1] |
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| **a** | (Flat Posterior) | |
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## Step 5: Synthesis and Context Switch (Document $z_1$ Dominates)
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To make the question even more specific and factual, the model uses the RAG-Token mechanism to dynamically incorporate a second, distinct fact from a different retrieved document ($z_1$).
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| **Token** | **Retrieved Context Domination** | **Action/Insight** |
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| :---: | :---: | :--- |
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| **"A** | **Document $z_1$ (High)** | The model shifts its focus to Document $z_1$, which mentions the second fact (*"A Farewell to Arms"*). This switch enables **knowledge synthesis**, a core strength of RAG, allowing it to combine multiple pieces of evidence into one coherent response.[1] |
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| **Farewell** | **Document $z_1$ (High)** | |
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| **to** | **Document $z_1$ (High)** | |
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| **Arms"** | **Document $z_1$ (High)** | |
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**Final Generated Question:**
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$$\text{"This author of 'The Sun Also Rises' is a novel by this author of 'A Farewell to Arms'"}$$
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**Intelligent Outcome:**
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The RAG-Token model successfully synthesized two separate facts from two different knowledge passages ($z_1$ and $z_2$) to create a highly specific and factually grounded question, a capability that purely parametric models often struggle with and one that an extractive model could not achieve.[1] This synthesis demonstrates how RAG strategically leverages both its explicit knowledge base (the non-parametric memory) and the LLM’s linguistic fluency (the parametric memory) to produce a superior, more diverse, and more factual output.[1]
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