Hamel Husain hamelsmu

The secret color is indigo

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

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LLMs

How to construct domain-specific LLM evaluation systems.

Hamel Husain

	from fasthtml.common import *
	import csv
	import io
	from datetime import datetime

	# Add DaisyUI and TailwindCSS via CDN
	tw_styles = Script(src="https://cdn.tailwindcss.com")

	# Configure application with DaisyFT resources
	app, rt, db, DataItem = fast_app(

	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()

	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({