This file contains all book-specific details for the textbook production pipeline.
The pipeline skill (textbook-chapter) and its agent definitions are generic and
reusable across any textbook project. This file is the only place where content
specific to THIS book lives.
When adapting the pipeline for a different book, create a new BOOK_CONFIG.md in
the new project's root directory with the same sections below.
- Title: Building Conversational AI using LLM and Agents
- Subtitle: A Practitioner's Guide to Large Language Models
- Target Audience: Software engineers with basic Python, familiar with APIs and JSON; basic linear algebra (vectors, matrices, dot products)
- Output Format: HTML chapter files linking to shared stylesheet
styles/book.css
- Illustrations: Warm, colorful, cartoon-like illustrations generated via Gemini API
- Application Examples: Teal/green color scheme
- Bibliographies: Card-based layout (
.bib-entry-card) - Epigraphs: Humorous quotes attributed to "A [Adjective] AI Agent/Model/etc."
All agents that need to reference other chapters (Cross-Reference, Bibliography, Narrative Continuity, etc.) use this canonical chapter map. This is the ACTIVE structure on disk. All agents should use this until migration to the proposed structure is complete.
Part 1: Foundations (part-1-foundations/)
00: ML & PyTorch Foundations module-00-ml-pytorch-foundations
01: NLP & Text Representation module-01-foundations-nlp-text-representation
02: Tokenization & Subword Models module-02-tokenization-subword-models
03: Sequence Models & Attention module-03-sequence-models-attention
04: Transformer Architecture module-04-transformer-architecture
05: Decoding & Text Generation module-05-decoding-text-generation
Part 2: Understanding LLMs (part-2-understanding-llms/)
06: Pretraining & Scaling Laws module-06-pretraining-scaling-laws
07: Modern LLM Landscape module-07-modern-llm-landscape
08: Reasoning & Test-Time Compute module-08-reasoning-test-time-compute
09: Inference Optimization module-09-inference-optimization
18: Interpretability module-18-interpretability
Part 3: Working with LLMs (part-3-working-with-llms/)
10: LLM APIs module-10-llm-apis
11: Prompt Engineering module-11-prompt-engineering
12: Hybrid ML + LLM module-12-hybrid-ml-llm
Part 4: Training & Adapting (part-4-training-adapting/)
13: Synthetic Data module-13-synthetic-data
14: Fine-Tuning Fundamentals module-14-fine-tuning-fundamentals
15: PEFT module-15-peft
16: Distillation & Merging module-16-distillation-merging
17: Alignment, RLHF & DPO module-17-alignment-rlhf-dpo
Part 5: Retrieval & Conversation (part-5-retrieval-conversation/)
19: Embeddings & Vector DBs module-19-embeddings-vector-db
20: RAG module-20-rag
21: Conversational AI module-21-conversational-ai
Part 6: Agentic AI (part-6-agentic-ai/)
22: AI Agents module-22-ai-agents
23: Tool Use & Protocols module-23-tool-use-protocols
24: Multi-Agent Systems module-24-multi-agent-systems
25: Specialized Agents module-25-specialized-agents
26: Agent Safety & Production module-26-agent-safety-production
Part 7: Multimodal & Applications (part-7-multimodal-applications/)
27: Multimodal module-27-multimodal
28: LLM Applications module-28-llm-applications
Part 8: Evaluation & Production (part-8-evaluation-production/)
29: Evaluation & Observability module-29-evaluation-observability
30: Observability & Monitoring module-30-observability-monitoring
31: Production Engineering module-31-production-engineering
Part 9: Safety & Strategy (part-9-safety-strategy/)
32: Safety, Ethics & Regulation module-32-safety-ethics-regulation
33: Strategy, Product & ROI module-33-strategy-product-roi
Part 10: Frontiers (part-10-frontiers/)
34: Emerging Architectures module-34-emerging-architectures
35: AI & Society module-35-ai-society
Part 11: From Idea to AI Product (part-11-idea-to-product/)
36: From Idea to Product Hypothesis module-36-idea-to-product
37: Building and Steering AI Products module-37-building-steering
38: Shipping and Scaling AI Products module-38-shipping-scaling
Note: Part 2 contains module-18 (Interpretability) and Part 6 contains module-23
(tool-use-protocols) alongside the legacy module-23 (multi-agent-systems). The canonical
module-23 is module-23-tool-use-protocols; the legacy module-23-multi-agent-systems
directory should be removed or merged into module-24-multi-agent-systems when convenient.
The following restructuring has been proposed but NOT yet executed on disk. Agents should continue using the Current Structure above until migration is complete. This section exists to document the plan and guide the Structural Architect (Agent #19) when the restructuring is approved.
Key changes (v3, based on competitive analysis of 11 books and 6 courses):
- AI Agents get their own dedicated Part (Part 6) with 4 chapters
- Interpretability moves from Training to Understanding (it explains models, not trains them)
- Data Engineering for LLMs added as new chapter (per LLM Engineer's Handbook, Chip Huyen)
- Structured Output made explicit in APIs chapter title
- Multimodal stays as its own topic (not merged into Part 2; requires Part 3-5 knowledge)
- Applications grouped by pattern (4 chapters: code, knowledge, enterprise, creative)
- LLMOps made explicit in Production chapter
- LLM Security made explicit in Safety chapter
- Voice/speech AI included in Conversational AI (given book title)
Part 1: Foundations (6 chapters, unchanged)
00: ML & PyTorch Foundations
01: NLP & Text Representation
02: Tokenization & Subword Models
03: Sequence Models & Attention
04: Transformer Architecture
05: Decoding & Text Generation
Part 2: Understanding LLMs (4 chapters, +1: Interpretability moved here)
06: Pretraining & Scaling Laws
07: Modern LLM Landscape (incl. reasoning models, SLMs, on-device)
08: Inference Optimization (incl. caching strategies, edge deployment)
09: Interpretability & Mechanistic Understanding [MOVED from Part 4]
Part 3: Working with LLMs (4 chapters, +1: Data Engineering added)
10: LLM APIs & Structured Output (incl. JSON mode, function calling)
11: Prompt Engineering & Advanced Techniques
12: Hybrid ML + LLM Architectures
13: Data Engineering for LLMs [NEW] (pipelines, quality, curation, governance)
Part 4: Training & Adapting (5 chapters, Interpretability moved out)
14: Synthetic Data Generation
15: Fine-Tuning Fundamentals
16: Parameter-Efficient Fine-Tuning (PEFT)
17: Distillation & Merging
18: Alignment: RLHF, DPO & Preference Tuning
Part 5: Retrieval & Conversation (3 chapters, unchanged)
19: Embeddings & Vector Databases
20: RAG (incl. long-context vs. RAG tradeoffs, GraphRAG)
21: Conversational AI (incl. voice/speech-to-speech, real-time)
Part 6: AI Agents (4 chapters, dedicated Part)
22: Agent Foundations, Protocols & Tool Use (MCP, A2A, AG-UI, ReAct)
23: Agent Memory, Planning & Reasoning (test-time compute, MemGPT/Letta)
24: Multi-Agent Systems (orchestration, debate, swarm, simulation)
25: Agent Applications (code agents, browser agents, scientific agents)
Part 7: Multimodal & Applications (5 chapters)
26: Multimodal Models (vision, audio, cross-modal, document AI)
27: Code & Development AI
28: Knowledge & Search AI
29: Enterprise AI Applications (healthcare, legal, finance, customer service)
30: Creative & Education AI
Part 8: Production & Strategy (3 chapters)
31: Production Engineering & LLMOps (experiment tracking, CI/CD, monitoring)
32: Safety, Security, Ethics & Regulation (LLM security, red teaming, EU AI Act)
33: Strategy, Product & ROI
Capstone:
34: Toward AGI (ARC-AGI benchmarks, scaling debate, emergent capabilities, alignment)
Total: 35 chapters across 8 Parts + capstone
Migration checklist (to execute when approved):
- Rename directories and files on disk
- Update all cross-references and navigation links
- Update the Current Structure section above (replace with this proposed structure)
- Update CROSS_REFERENCE_MAP.md with new section numbers
- Update CONFORMANCE_CHECKLIST.md book-specific sections
- Run Controller sweep to verify no broken links remain
- Create new chapter directories for: 13 (Data Engineering), 34 (Toward AGI)
- Split current Ch 25 (LLM Applications) into Chs 27-30
- Renumber current Ch 14-28 to new numbering scheme
- Same part:
../module-XX-name/index.html - Different part:
../../part-N-name/module-XX-name/index.html
When running agents across the entire book, partition by Part for parallelism:
- Batch A: Part 1 (Chapters 0-5, 6 modules)
- Batch B: Part 2 (Chapters 6-9 + 18, 5 modules)
- Batch C: Part 3 (Chapters 10-12, 3 modules)
- Batch D: Part 4 (Chapters 13-17, 5 modules)
- Batch E: Part 5 (Chapters 19-21, 3 modules)
- Batch F: Part 6 (Chapters 22-26, 5 modules)
- Batch G: Part 7 (Chapters 27-28, 2 modules)
- Batch H: Part 8 (Chapters 29-31, 3 modules)
- Batch I: Part 9 (Chapters 32-33, 2 modules)
- Batch J: Part 10 (Chapters 34-35, 2 modules)
These are book-specific humorous epigraph examples. Each chapter gets one epigraph attributed to a fictional AI persona using the "A [Adjective] [AI Role]" format.
- Tokenization: "I spent three hours debugging a Unicode error. Turns out the model thought an emoji was four separate tokens. It was, technically, correct." A Tokenizer Who Has Seen Things
- Attention: "They told me to attend to everything. So I did. Now I am 8 heads, none of which agree with each other." An Attention Head With Existential Questions
- Fine-tuning: "I was a perfectly good base model. Then they showed me 10,000 customer support transcripts and now I cannot stop being helpful." A Reluctantly Aligned Language Model
- Scaling laws: "More data. More parameters. More compute. At some point you stop asking 'will it work?' and start asking 'can we afford the electricity bill?'" A Mildly Concerned Cluster Administrator
- RAG: "I used to hallucinate confidently. Now I hallucinate with citations." An Unusually Honest Neural Network
- Agents: "They gave me tools, memory, and the ability to plan. I immediately got stuck in an infinite loop. Just like the humans, really." A Self-Aware ReAct Agent