Building LLMs for Production

Name: Building LLMs for Production
Price: 9 USD
Availability: InStock

0h 0m 0s

English

Paid

October 20, 2025

Course description

"Creating LLM for Production" is a practical 470-page guide (updated in October 2024) designed for developers and specialists who want to go beyond prototyping and build reliable, industry-ready applications based on large language models.

The book explains the fundamentals of how LLMs work and thoroughly examines key techniques: advanced prompting, Retrieval-Augmented Generation (RAG), model fine-tuning, evaluation methods, and deployment strategies. Readers gain access to interactive Colab notebooks, real code examples, and case studies demonstrating how to integrate LLMs into products and workflows in practice. Special attention is given to issues of security, monitoring, optimization, and cost reduction.

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#	Title
1	Table of Contents
2	About The Book
3	Introduction
4	Why Prompt Engineering, Fine-Tuning, and RAG?
5	Coding Environment and Packages
6	A Brief History of Language Models
7	What are Large Language Models?
8	Building Blocks of LLMs
9	Tutorial: Translation with LLMs (GPT-3.5 API)
10	Tutorial: Control LLMs Output with Few-Shot Learning
11	Recap
12	Understanding Transformers
13	Transformer Model’s Design Choices
14	Transformer Architecture Optimization Techniques
15	The Generative Pre-trained Transformer (GPT) Architecture
16	Introduction to Large Multimodal Models
17	Proprietary vs. Open Models vs. Open-Source Language Models
18	Applications and Use-Cases of LLMs
19	Recap
20	Understanding Hallucinations and Bias
21	Reducing Hallucinations by Controlling LLM Outputs
22	Evaluating LLM Performance
23	Recap
24	Prompting and Prompt Engineering
25	Prompting Techniques
26	Prompt Injection and Security
27	Recap
28	Why RAG?
29	Building a Basic RAG Pipeline from Scratch
30	Recap
31	LLM Frameworks
32	LangChain Introduction
33	Tutorial 1: Building LLM-Powered Applications with LangChain
34	Tutorial 2: Building a News Articles Summarizer
35	LlamaIndex Introduction
36	LangChain vs. LlamaIndex vs. OpenAI Assistants
37	Recap
38	What are LangChain Prompt Templates
39	Few-Shot Prompts and Example Selectors
40	What are LangChain Chains
41	Tutorial 1: Managing Outputs with Output Parsers
42	Tutorial 2: Improving Our News Articles Summarizer
43	Tutorial 3: Creating Knowledge Graphs from Textual Data: Finding Hidden Connections
44	Recap
45	LangChain’s Indexes and Retrievers
46	Data Ingestion
47	Text Splitters
48	Similarity Search and Vector Embeddings
49	Tutorial 1: A Customer Support Q&A Chatbot
50	Tutorial 2: A YouTube Video Summarizer Using Whisper and LangChain
51	Tutorial 3: A Voice Assistant for Your Knowledge Base
52	Tutorial 4: Preventing Undesirable Outputs with the Self-Critique Chain
53	Tutorial 5: Preventing Undesirable Outputs from a Customer Service Chatbot
54	Recap
55	From Proof of Concept to Product: Challenges of RAG Systems
56	Advanced RAG Techniques with LlamaIndex
57	RAG - Metrics & Evaluation
58	LangChain LangSmith and LangChain Hub
59	Recap
60	What are Agents: Large Models as Reasoning Engines
61	An Overview of AutoGPT and BabyAGI
62	The Agent Simulation Projects in LangChain
63	Tutorial 1: Building Agents for Analysis Report Creation
64	Tutorial 2: Query and Summarize a DB with LlamaIndex
65	Tutorial 3: Building Agents with OpenAI Assistants
66	Tutorial 4: LangChain OpenGPT
67	Tutorial 5: Multimodal Financial Document Analysis from PDFs
68	Recap
69	Understanding Fine-Tuning
70	Low-Rank Adaptation (LoRA)
71	Tutorial 1: SFT with LoRA
72	Tutorial 2: Using SFT and LoRA for Financial Sentiment
73	Tutorial 3: Fine-Tuning a Cohere LLM with Medical Data
74	Reinforcement Learning from Human Feedback
75	Tutorial 4: Improving LLMs with RLHF
76	Recap
77	Model Distillation and Teacher-Student Models
78	LLM Deployment Optimization: Quantization, Pruning, and Speculative Decoding
79	Tutorial: Deploying a Quantized LLM on a CPU on Google Cloud Platform (GCP)
80	Deploying Open-Source LLMs on Cloud Providers
81	Recap
82	Conclusion
83	Further Reading and Courses

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