Pages
463
Year
2024
Level
intermediate
Read time
12h
Louis-François Bouchard, Louie Peters · Towards AI · 2024
Reviewed by Ashish Sheth · Updated May 2026
Building LLMs for Production
Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG
4.8 / 5
AMAZON · 23 RATINGS
ai engineering · llm
SUBJECTS
What you'll come away with
01.
How to select the right LLM for your production use case
02.
Building reliable RAG systems with proper retrieval and indexing
03.
When to fine-tune vs use RAG vs prompt engineer
04.
Practical LangChain and LlamaIndex implementation patterns
05.
Quantization and distillation techniques for reducing inference costs
06.
How to build AI agents with tool access
Strengths
+Clear explanations with simple analogies for complex concepts
+Practical code examples using LangChain and LlamaIndex
+Strong chapters on fine-tuning, quantization, and distillation
+Endorsed by Jerry Liu (CEO of LlamaIndex) as most comprehensive LLM apps textbook
Caveats
−Title says 'Production' but lacks depth on actual hosting and serving infrastructure
−Reads more as applied research than a practical engineering guide
−Better as a reference than a cover-to-cover read
★ 4.8 FROM 23 READERS ON AMAZON
Check Price on Amazon →
Read this if
→Engineers building their first RAG or LLM-powered application
→ML practitioners wanting a survey of LLM techniques with code
→Teams deciding between fine-tuning, RAG, and prompting strategies
Skip this if
—Senior ML engineers already running LLM systems in production
—People wanting deep transformer theory (see Build a Large Language Model)
—Those looking for cloud infrastructure and DevOps guidance for LLMs
Head-to-head comparisons
Building LLMs for Production vs AI Engineering → Building LLMs for Production vs Designing Machine Learning Systems → Building LLMs for Production vs LLM Engineer's Handbook → Building LLMs for Production vs Hands-On Large Language Models → Building LLMs for Production vs AI Agents in Action → Frequently asked
Is Building LLMs for Production good for beginners?
You need basic Python and some understanding of what LLMs are. It's not a first-ever AI book, but it starts from fundamentals and builds up.
Does it cover the latest models like GPT-4 and Claude?
It covers LLM concepts and patterns that apply to any model. The frameworks (LangChain, LlamaIndex) work with all major providers.
Does it actually cover deploying LLMs to production servers?
Not deeply. Despite the title, the book leans toward applied techniques like prompting, fine-tuning, RAG, and distillation rather than the infrastructure side of serving models. For hosting and scaling, pair it with a cloud-specific guide or the LLM Engineer's Handbook, which is more deployment-focused.
Read this next
3 alternatives
Ready?
Check Price on Amazon →