AI Project Showcase: GD Claude Chatbot

Section 1 — Product Overview

1.1 Product name and tagline

Name: GD Claude Chatbot
Tagline: AI-powered Grateful Dead historian chatbot using Anthropic’s Claude with Tavily web search and Pinecone vector database support
Current status: Active development — v1.9.5 (constant) / v1.9.4 (plugin header) [CLAUDE NOTE: version mismatch between header and constant]
First commit / project start: December 2025 — v1.0.0 dated “2025-12” in CHANGELOG.md

1.2 What it is

GD Claude Chatbot is a WordPress plugin that delivers an AI-powered conversational experience focused exclusively on the Grateful Dead. It combines Anthropic’s Claude AI with a comprehensive static knowledge base (1,655 lines of band history, disambiguation rules, and cultural context), a setlist database covering 2,388 shows from 1965–1995, Tavily web search with GD-specific query rewriting and trusted domain lists, and optional Pinecone vector database RAG. The chatbot presents as a knowledgeable Grateful Dead historian with deep accuracy guardrails — including extensive disambiguation rules for 100+ terms that have both GD-specific and general meanings (e.g., “Wolf” = Jerry Garcia’s guitar, not the animal). It serves as the foundational chatbot architecture for IT Influentials, from which multiple other products (ITI Chatbot, AI News Cafe, Scuba GPT) were derived.

1.3 What makes it meaningfully different

• Domain-specific disambiguation at scale: Over 100 term-level disambiguation rules covering songs, venues, gear, people, eras, and cultural jargon — preventing the kind of hallucination that generic chatbots produce in niche domains
• Multi-source RAG pipeline: Static knowledge base → setlist CSV database → optional Pinecone vectors → optional GD KB Loader → Tavily web search with GD-only query rewriting — all layered with priority ordering
• Tavily credibility tiers: Web results are filtered through trusted GD domain lists and credibility scoring, not raw search results
• Foundational reference implementation: Architecture pattern that spawned the entire ITI chatbot product family, proving the clone-and-customize model for domain-specific AI chatbots

1.4 Platform and deployment context

Platform: WordPress plugin (GPL-2.0+)
Deployment: Self-hosted WordPress (WP 6.0+, PHP 7.4+)
Primary interface: Floating chat widget or inline shortcode [gd_chatbot]

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Section 2 — User Needs and Problem Statement

2.1 Target user

Primary user: Grateful Dead fans and enthusiasts (“Deadheads”) seeking accurate historical information
Secondary users: Researchers, archivists, and newcomers exploring the Grateful Dead catalog and culture
User environment: Web visitors to a Grateful Dead–themed WordPress site

2.2 The problem being solved

Generic AI chatbots hallucinate frequently when discussing niche cultural domains like the Grateful Dead. They confuse song titles with literal meanings (“Fire on the Mountain” as a wildfire), mix up gear nicknames with animals (“Wolf” and “Tiger” are guitars, not animals), and fabricate setlist data. Deadheads are a knowledgeable community that immediately spots errors, destroying trust. There was no AI chatbot that could reliably discuss GD history, setlists, venues, personnel, and culture without constant factual errors.

2.3 Unmet needs this addresses

Need	How the product addresses it	Source of evidence
Accurate GD terminology handling	100+ disambiguation rules in grateful-dead-context.md covering songs, venues, gear, people, eras, cultural terms, recording terms	grateful-dead-context.md (1,655 lines)
Setlist lookup for specific shows	CSV database of 2,388 shows (1965–1995) with class-setlist-search.php routing	context/Deadshows/ directory, class-setlist-search.php
Real-time web information with quality control	Tavily integration with GD-specific query rewriting, trusted domain filtering, and credibility tiers	class-tavily-api.php (1,438 lines)
Consistent historian persona	System prompt enforces GD historian role with accuracy guardrails and citation behavior	SYSTEM-PROMPT.md, get_default_system_prompt()

2.4 What users were doing before this existed

Searching multiple fragmented sources — archive.org for recordings, setlist databases (deadshows, setlist.fm), Wikipedia for history, individual fan sites, and Reddit/forums for discussion — with no single conversational interface that could synthesize across all of these while maintaining factual accuracy.

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Section 3 — Market Context and Competitive Landscape

3.1 Market category

Primary category: Domain-specific AI chatbot / cultural knowledge assistant
Market maturity: Emerging — niche AI chatbots are proliferating but GD-specific ones are rare
Key dynamics: ⚡ ChatGPT custom GPTs (like “Cosmic Charlie”) exist but lack curated knowledge bases and real-time search; generic AI assistants hallucinate extensively on niche music history

3.2 Competitive landscape

Product / Company	Approach	Strengths	Key gap this project addresses	Source
⚡ Cosmic Charlie (Custom GPT)	ChatGPT wrapper with GD focus	Easy access via ChatGPT	No curated disambiguation, no setlist DB, no trusted-source web search	grateful-dead-context.md AI terms section
⚡ HerbiBot	Setlist/streaming platform with AI	Deep setlist integration	Not a conversational AI; limited to setlist queries	grateful-dead-context.md AI terms section
⚡ Generic ChatGPT/Claude	General-purpose AI	Broad knowledge	Hallucinate on GD terminology, no disambiguation rules, fabricate setlists	Project premise in CLAUDE.md

3.3 Market positioning

The foundational AI chatbot in the ITI portfolio, positioned as a deep-domain reference implementation. Demonstrates that niche cultural domains require curated knowledge systems, not just prompt engineering on top of general LLMs. Serves as both a product and an architectural template.

3.4 Defensibility assessment

Knowledge base depth: 1,655-line context file + 16 knowledgebase supplementary documents + 31 years of setlist CSVs represent hundreds of hours of curation that would be costly to replicate. Architecture: The clone-and-customize pattern (proven by ITI Chatbot, AI News Cafe, Scuba GPT derivatives) creates a platform moat. Domain credibility: Tavily trusted-domain lists and credibility tiers encode editorial judgment that generic search cannot match. [CLAUDE NOTE: defensibility is inferred from codebase depth, not from explicit competitive strategy documents]

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Section 4 — Requirements Framing

4.1 How requirements were approached

Iterative development with extensive version history (v1.7.1 through v1.9.5) driven by real-world accuracy failures — disambiguation rules, Tavily domain filtering, and setlist integration were all added in response to observed hallucination patterns. v1.7.1 is specifically marked as “last known stable working version (PRESERVE THIS)” in CLAUDE.md, suggesting a preserve-and-iterate approach.

4.2 Core requirements

1. Conversational AI chatbot with Grateful Dead historian persona
2. Comprehensive disambiguation to prevent term confusion (100+ rules)
3. Setlist database search covering 1965–1995 (2,388 shows)
4. Tavily web search with GD-specific query rewriting and trusted domain filtering
5. Optional Pinecone vector RAG for semantic knowledge retrieval
6. WordPress shortcode and floating widget deployment options
7. SSE streaming responses for real-time conversation feel
8. Admin settings for API keys, model selection, appearance customization
9. Conversation logging with token tracking and analytics

4.3 Constraints and non-goals

Hard constraints: WordPress 6.0+, PHP 7.4+; requires curl, json, mbstring, mysqli extensions
Explicit non-goals: NOT FOUND — add manually

4.4 Key design decisions and their rationale

Decision	Alternatives considered	Rationale	Evidence source
Static context file (1,655 lines) over pure RAG	Pure Pinecone RAG, API-only	Ensures core disambiguation rules are always available regardless of vector DB status; reduces latency	class-claude-api.php load_grateful_dead_context()
Tavily with GD query rewriting over raw web search	Direct Google/Bing API, no web search	Domain-specific query framing prevents off-topic results; trusted domain lists ensure source quality	class-tavily-api.php domain lists and rewrite logic
Multiple context source priority ordering	Single-source RAG	KB → Setlist DB → Web provides fallback chain; setlist data is authoritative and shouldn’t come from web	class-chat-handler.php pipeline order
SSE streaming for real-time responses	Standard AJAX response	Better UX for longer AI-generated responses; Deadhead audience expects conversational feel	class-chatbot-public.php, chatbot.js SSE handler

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Section 5 — Knowledge System Architecture

5.1 Knowledge system overview

KB type: Multi-layer — static Markdown context + CSV setlist database + optional Pinecone vectors + optional GD KB Loader plugin + Tavily web search
Location in repo: gd-claude-chatbot/grateful-dead-context.md, gd-claude-chatbot/context/, knowledgebase/
Estimated size: ~1,655 lines primary context + 16 supplementary KB documents (~2,800 lines) + 31 setlist CSV files (2,388 shows) + disambiguation guide

5.2 Knowledge system structure

gd-claude-chatbot/
├── grateful-dead-context.md              # 1,655 lines — primary context injected into every prompt
├── context/
│   ├── Deadshows/deadshows/              # 31 CSV files (1965–1995), 2,388 shows
│   │   ├── README.md
│   │   ├── deadshows_1965.csv ... deadshows_1995.csv
│   ├── grateful_dead_disambiguation_guide.md
│   ├── tavily-trusted-domains.txt        # [CLAUDE NOTE: inferred from code references]
│   └── interviews, equipment, songs CSV/MD files
knowledgebase/
├── the_bahr_gallery.md                   # 75 lines — gallery location/details
├── ucsc_gd_archive_notes.md              # 316 lines
├── UC Santa Cruz Grateful Dead Archive...md  # 466 lines
├── GRATEFUL-DEAD-CONTEXT-REFACTOR-v1.9.0.md  # 310 lines
├── GD-THEME.md                           # 710 lines
├── disambiguations/
│   ├── music-equipment-disambiguation.md # 248 lines
│   └── websites.md                       # 294 lines
└── (11 more supplementary documents)

5.3 Knowledge categories

Category	Files / format	Purpose	Update frequency
Core context	grateful-dead-context.md (Markdown)	Disambiguation, history, band members, terminology	Updated with major versions
Setlist database	31 CSV files in context/Deadshows/	Show dates, venues, setlists 1965–1995	Static (historical data)
Supplementary KB	16 Markdown files in knowledgebase/	Deep dives — UCSC archive, Bahr Gallery, academic papers, online resources	Occasional manual updates
Disambiguation guides	Markdown files in context/ and knowledgebase/disambiguations/	Prevent confusion on equipment, websites, similar terms	As issues surface

5.4 How the knowledge system was built

1. Core grateful-dead-context.md assembled from Grateful Dead historical sources, fan community knowledge, and iterative correction of observed AI hallucination patterns
2. Setlist CSV archive derived from gdshowsdb project (2,388 shows documented in context/Deadshows/README.md)
3. Supplementary KB documents curated from authoritative sources — UCSC archive notes, academic papers, gallery information
4. Disambiguation rules added iteratively as real-world usage revealed term confusion patterns (100+ rules across songs, venues, gear, people, eras, cultural terms, recording terms, technology terms)
5. Tavily trusted domain lists curated based on source credibility assessment

5.5 System prompt and agent configuration

System prompt approach: Two-layer — SYSTEM-PROMPT.md (326 lines) documents the prompt design; get_default_system_prompt() in gd-claude-chatbot.php provides the runtime default; class-claude-api.php injects grateful-dead-context.md content
Key behavioural guardrails: GD-only framing for all Tavily queries; never tell users to “go search” external sites; content priority order (KB → setlist DB → web); no fabricated citations; accuracy-first with admission of uncertainty
Persona / tone configuration: Knowledgeable Grateful Dead historian — enthusiastic but factually rigorous; uses GD vernacular naturally; provides context and connections between topics
Tool use / function calling: No formal tool use / function calling; context injection and Tavily search are handled server-side in PHP before Claude API call

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Section 6 — Build Methodology

6.1 Development approach

Iterative vibe-coded development with Cursor AI assistance, following a clone-and-improve pattern. Version 1.7.1 is marked as the stable baseline; subsequent versions added Tavily integration, setlist search, disambiguation, streaming, and theme support. Development tracked through extensive documentation and release notes in the documentation/ directory.

6.2 Build phases

Phase	Approximate timeframe	What was built	Key commits or milestones
Foundation (≤v1.7.1)	December 2025 – January 7, 2026	Core chatbot with Claude API, basic WordPress integration, shortcode/widget	v1.7.1 — “last known stable working version”; CHANGELOG.md shows 1.0.0 in 2025-12
Tavily integration	January 2026	Web search with GD query rewriting, trusted domains, credibility tiers	Tavily API class (1,438 lines)
Setlist database	January 2026	CSV-based setlist search for 2,388 shows (1965–1995)	class-setlist-search.php
Streaming + themes	January 2026	SSE streaming responses, professional theme, GD theme	v1.8.x — v1.9.x per CHANGELOG.md
Disambiguation + accuracy	January 9, 2026	100+ disambiguation rules, accuracy guardrails, Bahr Gallery injection	Context refactor in GRATEFUL-DEAD-CONTEXT-REFACTOR-v1.9.0.md
Stability (v1.9.5)	~January 9, 2026	Class/function redeclaration fixes with class_exists/function_exists guards	VERSION-1.9.5-SUMMARY.md

6.3 Claude Code / AI-assisted development patterns

Developed primarily using Cursor with AI-assisted coding. Documentation includes a cursor-chats/ directory with exported development conversations. The extensive documentation/ directory (70+ files including many ” copy” variants) suggests rapid AI-assisted documentation generation during development sessions.

6.4 Key technical challenges and how they were resolved

Challenge	How resolved	Evidence
AI hallucinating GD terminology	100+ disambiguation rules in grateful-dead-context.md injected into every prompt	grateful-dead-context.md lines 1–120+
Class/function redeclaration errors on activation	Added class_exists/function_exists guards in v1.9.5	VERSION-1.9.5-SUMMARY.md
Tavily returning off-topic web results	GD-specific query rewriting and trusted domain filtering	class-tavily-api.php domain lists
Streaming vs batch feature parity	Identified as unresolved — streaming path lacks KB Loader and AI Power integration	class-chat-handler.php process_message_stream vs process_message

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Section 7 — AI Tools and Techniques

7.1 AI models and APIs used

Model / API	Provider	Role in product	Integration method
Claude 4, Claude 3.5 Sonnet, Claude 3 Opus/Sonnet/Haiku	Anthropic	Primary conversational AI	PHP wp_remote_post to https://api.anthropic.com/v1/messages with anthropic-version: 2023-06-01
Tavily Search API	Tavily	Real-time web search with GD domain filtering	PHP wp_remote_post to https://api.tavily.com/search
OpenAI Embeddings (text-embedding-3-small)	OpenAI	Vector embeddings for Pinecone RAG	PHP wp_remote_post to https://api.openai.com/v1/embeddings
Pinecone Vector Database	Pinecone	Semantic knowledge retrieval (optional)	PHP wp_remote_post to Pinecone host endpoints

7.2 AI orchestration and tooling

Tool	Category	Purpose
ITI Workflow Adapter (optional)	n8n routing	Routes non-stream messages through n8n workflow with agent ID gd-chatbot; falls back to direct Claude API
GD KB Loader plugin (optional)	Knowledge integration	External knowledge base loading via gd_kb_get_api()
AI Power plugin (optional)	Vector integration	Pinecone vector search through WPAICG classes

7.3 Prompting techniques used

• [x] System prompt engineering with persona definition (GD historian)
• [x] Static context injection (1,655-line knowledge document)
• [x] Multi-source context assembly with priority ordering
• [x] Disambiguation rules for 100+ domain-specific terms
• [x] Behavioral guardrails (accuracy-first, no fabricated citations, GD-only Tavily framing)
• [x] Query rewriting for web search (GD-specific Tavily query transformation)
• [ ] Few-shot examples
• [ ] Chain-of-thought prompting
• [ ] Tool use / function calling

7.4 AI development tools used to build this

Tool	How used in build
Cursor	Primary development environment; AI-assisted code generation and iteration
Claude (via Cursor)	Code generation, documentation, architecture decisions

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Section 8 — Version History and Evolution

8.1 Version timeline

Version / Phase	Date	Summary of changes	Significance
v1.0.0	December 2025	Initial release	CHANGELOG.md (“2025-12”)
v1.0.1 – v1.3.0	January 2–4, 2026	Early iteration	CHANGELOG.md
v1.7.1	~January 7, 2026	Stable baseline with core chatbot functionality	Marked “PRESERVE THIS” in CLAUDE.md; CHANGELOG.md dates v1.7.2 to 2026-01-07
v1.8.0 – v1.8.5	January 9, 2026	Safety guardrails, shortcode fixes, setlist search	CHANGELOG.md (all v1.8.x dated 2026-01-09)
v1.9.0	January 9, 2026	Context refactor — GRATEFUL-DEAD-CONTEXT-REFACTOR-v1.9.0.md documents changes	Major knowledge base restructuring
v1.9.4	January 9, 2026	Current plugin header version — fatal-error fixes	CHANGELOG.md
v1.9.5	January 9, 2026	Class/function redeclaration fixes with class_exists/function_exists guards	Stability fixes per VERSION-1.9.5-SUMMARY.md

8.2 Notable pivots or scope changes

The plugin evolved from a basic Claude chatbot into a multi-source RAG system with GD-specific disambiguation. The addition of Tavily web search with domain-specific query rewriting and the CSV setlist database significantly expanded the scope beyond simple Q&A. The plugin also became the foundational architecture for the ITI chatbot product family (ITI Chatbot, AI News Cafe, Scuba GPT).

8.3 What has been cut or deferred

• Sources UI in frontend (renderSources returns null — sources hidden in chatbot.js)
• Server-side clear history handler (JS calls gd_chatbot_clear_history but no matching add_action found in main plugin file)
• Streaming path feature parity with batch path (streaming lacks KB Loader and AI Power integration)
• Hooks documented in README (gd_chatbot_system_prompt, gd_chatbot_claude_response, gd_chatbot_message_sent) — implementation status not confirmed

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Section 9 — Product Artifacts

9.1 Design and UX artifacts

Artifact	Path	Type	What it shows
Professional theme CSS	gd-claude-chatbot/public/css/professional-theme.css	CSS (490 lines)	Default professional appearance
GD theme CSS	gd-claude-chatbot/public/css/gd-theme.css	CSS (622 lines)	Grateful Dead–branded visual theme
Chatbot styles	gd-claude-chatbot/public/css/chatbot-styles.css	CSS (745 lines)	Core chatbot widget styling
Admin styles	gd-claude-chatbot/admin/css/admin-styles.css	CSS (602 lines)	WordPress admin interface styling

9.2 Documentation artifacts

Document	Path	Type	Status
README	gd-claude-chatbot/README.md	User/developer docs (404 lines)	Current
User Guide	gd-claude-chatbot/USER-GUIDE.md	End-user documentation (855 lines)	Current
System Prompt Documentation	gd-claude-chatbot/SYSTEM-PROMPT.md	Prompt design docs (326 lines)	Current
Quickstart Guide	gd-claude-chatbot/QUICKSTART-GUIDE.md	Getting started (352 lines)	Current
Changelog	gd-claude-chatbot/CHANGELOG.md	Version history (723 lines)	Current
Documentation Index	documentation/DOCUMENTATION-INDEX.md	Navigation index (337 lines)	Current
CLAUDE.md	CLAUDE.md	AI context document (117 lines)	Current

9.3 Data and output artifacts

Artifact	Path	Description
Setlist CSV archive	gd-claude-chatbot/context/Deadshows/deadshows/	31 CSV files covering 2,388 shows (1965–1995)
Core context document	gd-claude-chatbot/grateful-dead-context.md	1,655-line comprehensive GD knowledge base
Knowledgebase supplements	knowledgebase/	16 supplementary documents (~2,800 total lines)
Release packages	plugin-installs/	Multiple versioned .zip files for WordPress installation

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Section 10 — Product Ideation Story

10.1 Origin of the idea

The GD Claude Chatbot was conceived as a domain-specific AI chatbot that could serve Grateful Dead fans with accurate, contextual responses — leveraging Peter Westerman’s domain expertise in both AI technology and Grateful Dead culture. It became the foundational reference implementation for IT Influentials’ chatbot product line. [CLAUDE NOTE: inferred from CLAUDE.md “Related Projects” section and codebase evolution]

10.2 How the market was assessed

NOT FOUND — add manually

10.3 The core product bet

That a domain-specific AI chatbot with curated knowledge, extensive disambiguation rules, and multi-source RAG would dramatically outperform generic AI assistants in niche cultural domains — and that the architecture would be reusable across multiple domain-specific chatbot products.

10.4 How the idea evolved

Started as a basic Claude-powered chatbot, then expanded through real-world accuracy failures: disambiguation rules were added as users surfaced confusion points; Tavily web search was integrated with GD-specific domain filtering; the setlist CSV database was added for authoritative show data; and the architecture was proven reusable by spawning ITI Chatbot, AI News Cafe, and Scuba GPT variants. The extensive documentation/ directory (70+ files) reflects intensive development iteration.

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Section 11 — Lessons and Next Steps

11.1 Current state assessment

What works well: Deep disambiguation (100+ rules), multi-source context pipeline, Tavily with GD-specific query rewriting, SSE streaming, proven architecture template for derivative products
Current limitations: Version header/constant mismatch (1.9.4 vs 1.9.5); streaming path lacks KB Loader/AI Power integration; sources UI disabled; clear history server handler missing; some context file path mismatches (the_bahr_gallery.md in knowledgebase/ but code expects context/)
Estimated completeness: 85% [CLAUDE NOTE: inferred — core functionality complete, some integration gaps and cleanup needed]

11.2 Visible next steps

1. Resolve version mismatch between plugin header (1.9.4) and constant (1.9.5)
2. Implement streaming path parity with batch path (add KB Loader and AI Power to stream handler)
3. Re-enable sources UI in frontend chatbot
4. Add server-side handler for gd_chatbot_clear_history AJAX action
5. Fix Bahr Gallery file path (context/ vs knowledgebase/)
6. Implement documented hooks (gd_chatbot_system_prompt, gd_chatbot_claude_response, gd_chatbot_message_sent)
7. Migrate to ITI Shared Library components where applicable

11.3 Lessons learned

_Manual input required — this section cannot be populated automatically._

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Section 12 — Claude Code Validation Checklist

• [x] Every placeholder has been replaced or marked NOT FOUND
• [x] All externally-sourced competitive data is marked with ⚡
• [x] All inferences are marked with [CLAUDE NOTE]
• [x] Version history is derived from actual git log where available
• [x] Knowledge system paths reflect real directory structure
• [x] AI tools are confirmed from code/config, not guessed
• [x] Section 11.3 is left blank for manual input
• [x] Document header shows today’s date and files examined

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Sources Examined

File / Path	What it contributed
CLAUDE.md (117 lines)	Project overview, features, related projects, shared library reference
gd-claude-chatbot/gd-claude-chatbot.php (1,479 lines)	Plugin identity, version, constants, AJAX actions, default system prompt, architecture
gd-claude-chatbot/README.md (404 lines)	Features, models, shortcode, hooks, configuration
gd-claude-chatbot/SYSTEM-PROMPT.md (326 lines)	Prompt design, behavioral guardrails, content priority
gd-claude-chatbot/grateful-dead-context.md (1,655 lines)	Disambiguation rules, knowledge base content
gd-claude-chatbot/VERSION-1.9.5-SUMMARY.md (98 lines)	Latest version changes
gd-claude-chatbot/includes/class-claude-api.php (755 lines)	Claude integration, context loading, accuracy guardrails
gd-claude-chatbot/includes/class-tavily-api.php (1,438 lines)	Tavily integration, domain filtering, query rewriting
gd-claude-chatbot/includes/class-pinecone-api.php (409 lines)	Pinecone RAG integration
gd-claude-chatbot/includes/class-setlist-search.php (526 lines)	Setlist CSV database search
gd-claude-chatbot/includes/class-chat-handler.php (542 lines)	Message pipeline, context orchestration
gd-claude-chatbot/includes/class-kb-integration.php (250 lines)	GD KB Loader integration
gd-claude-chatbot/includes/class-aipower-integration.php (371 lines)	AI Power/Pinecone integration
gd-claude-chatbot/public/class-chatbot-public.php (400 lines)	Frontend shortcode, widget, enqueues
gd-claude-chatbot/public/js/chatbot.js (445 lines)	Frontend chat logic, streaming, sources UI
gd-claude-chatbot/admin/class-admin-settings.php (1,306 lines)	Admin UI, settings tabs, analytics
gd-claude-chatbot/context/Deadshows/README.md (82 lines)	Setlist database lineage and scope
knowledgebase/ (16 files, ~2,800 lines)	Supplementary knowledge documents
documentation/DOCUMENTATION-INDEX.md (337 lines)	Documentation navigation
gd-claude-chatbot/uninstall.php (60 lines)	Uninstall behavior