hamelsmu

Success Stories of Fine-Tuning LLMs Across Industries

Below is a summary of diverse use cases where companies fine-tuned large language models (LLMs) to solve business challenges that previous methods struggled with. Each case highlights the challenge, the fine-tuning approach, and the key results achieved.

Summary of Fine-Tuning Success Cases

Use Case	Key Results	Source Link
Wealth Management Assistant (Finance)	98% advisor adoption; document access up from 20% to 80%	OpenAI & Morgan Stanley
Insurance Claims AI (Insurance)	30% accuracy improvement vs. generic LLMs	[Insurance News (EXL)](https://www.insurancenews.c

hamelsmu

hamelsmu

from html2text import HTML2Text
from textwrap import dedent
import re

def get_md(cts, extractor='h2t'):
    h2t = HTML2Text(bodywidth=5000)
    h2t.ignore_links,h2t.mark_code,h2t.ignore_images = (True,)*3
    res = h2t.handle(cts)
    def _f(m): return f'```\n{dedent(m.group(1))}\n```'
    return re.sub(r'\[code]\s*\n(.*?)\n\[/code]', _f, res or '', flags=re.DOTALL).strip()

hamelsmu

def follow_user_follows(client, target_user):
    "Follow everyone the target_user is following."
    
    cursor = None
    total_followed = 0

    while True:
        # Step 1: Fetch a batch of accounts the target user is following
        # https://docs.bsky.app/docs/api/app-bsky-graph-get-follows
        response = client.app.bsky.graph.get_follows({

hamelsmu

Read docs

Modern web applications in pure Python

Built on solid web foundations, not the latest fads - with FastHTML you can get started on anything from simple dashboards to scalable web applications in minutes.

Learn more Watch intro 7min 30sec

Try now

hamelsmu

Practical Deep Learning for Coders

• __

__

1. Practical Deep Learning

• Practical Deep Learning

hamelsmu

fast.ai

• About
• __
• __
• __

The First Certificate in Deep Learning

courses

hamelsmu

Your AI Product Needs Evals

LLMs

How to construct domain-specific LLM evaluation systems.

Author

Hamel Husain

hamelsmu

hamelsmu

Structured Outputs

Introduction

JSON is one of the most widely used formats in the world for applications to exchange data.

Structured Outputs is a feature that ensures the model will always generate responses that adhere to your supplied JSON Schema, so you don't need to worry about the model omitting a required key, or hallucinating an invalid enum value.

Some benefits of Structed Outputs include: