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.
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.
Reference price: $59.99
Price checked: Nov 14, 2026
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Pros and cons
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.
Technical specifications
8 specifications| Publisher | Packt Publishing |
|---|---|
| Publication date | July 30, 2026 |
| Language | English |
| Print length | 554 pages |
| ISBN-10 | 1837024537 |
| ISBN-13 | 978-1837024537 |
| Weight listed | 2.07 pounds |
|---|---|
| Dimensions | 7.5 x 1.25 x 9.25 inches |
What to know before buying
- 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.
Who is it for?
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 reviews on Amazon
- 5 stars
64% 64% - 4 stars
36% 36% - 3 stars
0% 0% - 2 stars
0% 0% - 1 star
0% 0%
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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