# Large Language Models in Finance: Hands-on LLM Guide

Hands-on: Large Language Models in Finance is a practical guide to designing and applying LLM systems in financial workflows. It covers RAG, fine-tuning, agents, evaluation, and governance, with Python examples and case studies. The listing says readers should already understand Python, machine learning, and financial markets.

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- Market: US
- Language: en-US
- Currency: USD
- Category: Books, Music & Hobbies &gt; Books, Music & Movies &gt; Books & Comics
- Price: 49.99 USD
- Previous price: 59.99 USD
- Discount: 17%
- Price checked: 2026-11-14T05:50:25.681Z
- Rating: 4.6 / 5
- Reviews: 9

Prices and deal status are the latest recorded data, not a guarantee of availability. Confirm the price and availability with the store.

## At a glance

- Topics include transformer foundations, prompting, RAG, fine-tuning, and financial LLM system design.
- Explores applications across trading, investment research, banking, risk, fraud, compliance, KYC, AML, and document intelligence.
- Covers financial agents, MCP, tool use, orchestration, memory, evaluation, governance, and monitoring.
- The listing describes hands-on Python examples, financial case studies, and a companion GitHub repository.

## Pros

- Connects LLM engineering topics to concrete financial domains and workflows.
- Includes attention to financial risks such as leakage, hallucinations, backtesting contamination, and temporal validation.
- Addresses governance, auditability, monitoring, and human oversight alongside implementation.

## Specifications

### Book details
- Publisher: Packt Publishing
- Publication date: July 30, 2026
- Language: English
- Print length: 554 pages
- ISBN-10: 1837024537
- ISBN-13: 978-1837024537
### Dimensions and installation
- Weight listed: 2.07 pounds
- Dimensions: 7.5 x 1.25 x 9.25 inches

## Before you buy

- The listing says readers should have a fundamental understanding of Python, machine learning, and financial markets, so this is not positioned as a first introduction to those subjects. The stated publication date is July 30, 2026.

## A good fit for

- Data scientists, quantitative analysts, AI engineers, fintech developers, and software architects applying LLMs to finance.
- Banking, trading, investment, risk, fraud, and compliance practitioners seeking technical workflows.
- Readers with basic Python, machine-learning, and financial-market knowledge.

## Less suited to

- Readers seeking a beginner-level introduction to Python, machine learning, or financial markets.
- Readers looking for a general-purpose LLM guide without a finance focus.

## Customer feedback

Aggregate ratings lean positive, with ratings in the four- and five-star categories, but the sample is small and no written customer feedback is supplied. Those ratings do not establish how useful the Python examples or finance-specific coverage are for a particular reader.

## Frequently asked questions

### What topics does this book cover?
The listing describes LLM foundations and system design, RAG, fine-tuning, financial agents, MCP, applications across finance, and evaluation and governance.
### Does it include practical coding material?
The listing says it includes hands-on Python examples, case studies, and a companion GitHub repository.
### What background does the listing recommend?
It says readers should have a fundamental understanding of Python programming, machine learning, and financial markets.
### Is a digital copy included with the print book?
The listing claims that purchase of the print or Kindle book includes a free PDF eBook. Check the selected edition's terms before buying.

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## Images

![Large Language Models in Finance: Hands-on LLM Guide — 1](https://best-deals.pro/i/VRBpXAuBPf9oHXEj9ESWo6ql6uQT8ae8Ts2fUQvGGpEWXZ0QrhUOX5-hJkilyqI0wq6kALz6KJC3SELHD5fnH-vdAyEAZGUE9uymaVDWc6rdw__9UDeF.jpg)
