Jeremy Brayton w0rd-driven

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

Efficient Group Updates with Nested Kino Frames

Mix.install([
  {:kino, "~> 0.14.2"},
  {:kino_user_presence, "~> 0.1.2"}
])

I have been utilizing artificial intelligence to enhance and optimize my codebases. After evaluating various models, applications, and editors, I find the claude.ai interface with a Pro Account to be the most effective. Here’s the approach I’ve developed to achieve optimal results:

This gist contains a carefully crafted prompt and a script designed to convert your entire Laravel codebase (excluding the resources folder, which can be easily added if needed) into a TXT file with the following structure:

<File Start: ./path/filename.extension> Content of file <End File: ./path/filename.extension>

To implement this method:

LiveView Forms

Application.put_env(:sample, Example.Endpoint,
  http: [ip: {127, 0, 0, 1}, port: 5001],
  server: true,
  live_view: [signing_salt: "aaaaaaaa"],
  secret_key_base: String.duplicate("a", 64)
)

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet

	#include <stdio.h>
	#include <windows.h>

	#pragma comment(lib, "winmm.lib")

	void Nothing(WORD wKey)
	{
	}

	void PrintKey(WORD wKey)

	@default_timeout 100
	@check_interval 10

	# Test Helpers

	defp wait_for(fun, timeout \\ @default_timeout) do
	start_time = System.monotonic_time(:millisecond)
	ref = make_ref()

	try do

	import { useEffect, useRef } from 'react';

	const useInactivityTimeout = (timeoutInHours) => {
	const timeoutInMillis = timeoutInHours * 60 * 60 * 1000;
	const timer = useRef(null);

	useEffect(() => {
	if (navigator.userActivation) {
	if(navigator.userActivation.isActive){
	if (timer.current) {

	defmodule OperationsTest do
	use ExUnit.Case, async: true

	def make_mod() do
	String.to_atom("Elixir.Test#{System.unique_integer([:positive])}")
	end

	describe "operation/2" do
	setup do
	mod = make_mod()

	<!-- livebook:{"persist_outputs":true} -->

	# Distribute

	## Section

	A small toy to show how you might, given a stream, do a "fan out", processing different elements in separate streams. Powered by simple primitives like `Stream.resource` and `spawn_link`.

	```elixir
	defmodule Distribute do