Published by Philip Creutzmann at


LLMs in Business Part 2: Automating Success

In today’s fast-paced business environment, professionals and organizations are continually confronted with challenges that demand innovative solutions. Fortunately, the rise of Large Language Models (LLMs) such as GPT-3 offers a transformative way to address these issues. These advanced AI systems are proficient in understanding and generating human-like text, making them invaluable tools for a multitude of tasks. In this blog post, we’ll explore common problems in business and professional settings that can be effectively resolved with LLM assistance.

Meeting Minute Generation

Challenge: Businesses often grapple with the time-consuming task of recording and summarizing meeting minutes. This process is vital for maintaining documentation, tracking action items, and ensuring effective communication.

Solution: LLMs can be harnessed to automate meeting minute generation. They have the capability to transcribe spoken words into text, identify key discussion points, and summarize them into comprehensive meeting minutes. This not only saves time but also enhances accuracy, ensuring that nothing gets lost in translation.

We recently developed a LLM service for one of our valued clients to automate the generation of meeting minutes based on teams-meeting transcripts. This solution not only delivers a comprehensive meeting summary but also identifies action items in a predefined format. The structured output seamlessly integrates with their project management tool, enabling automatic task suggestions and task creation.

Email Writing

Challenge: Crafting clear, concise, and persuasive emails can be challenging, particularly when communicating with clients, partners, or colleagues. Poorly written emails can lead to misunderstandings and communication breakdowns.

Solution: LLMs come to the rescue by assisting in email composition. They can suggest improvements, rephrase sentences, and even generate entire email drafts. This ensures that messages are well-written, professional, and effective in conveying the intended message, fostering better communication.

Content Rephrasing and Paraphrasing

Challenge: Content creators often need to rephrase or paraphrase text for various reasons, such as avoiding plagiarism, improving readability, or adapting content for different audiences.

Solution: LLMs streamline this process by offering alternative phrasings, synonyms, and restructured sentences. This simplifies the task of generating original content or adapting existing material while ensuring clarity and coherence. Moreover, this solution can be integrated into editing tools and can be tailored to match your corporate tone with the use of sample text.

Report and Document Summarization

Challenge: Businesses grapple with vast amounts of information, and professionals frequently need to extract key insights or summarize lengthy reports and documents for decision-makers.

Solution: LLMs can analyze documents and generate concise summaries that capture essential points and findings. This enables more efficient information consumption and decision-making, helping organizations stay ahead of the curve.

Content Moderation

Challenge: Online platforms face the daunting challenge of moderating user-generated content to prevent the spread of inappropriate or harmful material.

Solution: LLMs can be enlisted to automatically review and moderate content, flagging or removing content that violates community guidelines. This ensures a safe and compliant online environment, safeguarding both users and the platform’s reputation.

Function Calling: One Endpoint to rule them all

Challenge: Many processes require structured information in a well-defined format. Extracting and organizing information from text can be both time-consuming and frustrating, often left to human intervention.

Solution: One great super flexible way to automate processes with LLMs is what OpenAI calls “function call”. The idea is to use a prompt that makes the model behave like a function with a defined input, e.g. an email or document and a defined output, e.g. a dictionary structure. This provides a very flexible approach for information extraction. To make it work one needs to provide as little as an output schema with optional descriptions.

Prompt Example: Information Extraction

Extract information from the document below. The output should be formatted as a JSON instance that conforms to the JSON schema below. 

document = {
    "document_type": "email",
    "text_body": "Subject: Request for New Blog Posts on Large Language Models Dear Philip, I hope this email finds you well. I'm a dedicated reader of Data Insights's blog, and I greatly appreciate the expertise and insights your team consistently delivers, especially in the domain of large language models. I'm writing to request more blog posts specifically focused on large language models. Given the dynamic nature of this field and its relevance across various industries, I believe that your thought leadership can offer valuable guidance and knowledge to your readers, including myself. Thanks and warm regards, John Smith Email: Phone: (123) 456-7890"

response_schema = {
    "properties": {
        "Email Details": {
            "description": "Details about the email itself",
            "properties": {
                "Date": {
                    "description": "The date when the email was sent",
                    "title": "Date",
                    "type": "string"
                "Subject": {
                    "description": "The subject of the email",
                    "title": "Subject",
                    "type": "string"
            "required": [
            "title": "Email Details",
            "type": "object"
        "Recipient Information": {
            "description": "Details about the email recipient",
            "properties": {
                "Recipient's Name": {
                    "description": "The name of the email recipient",
                    "title": "Recipient's Name",
                    "type": "string"
            "required": [
                "Recipient's Name"
            "title": "Recipient Information",
            "type": "object"
        "Sender Information": {
            "description": "Details about the email sender",
            "properties": {
                "Sender's Email": {
                    "description": "The email address of the sender",
                    "title": "Sender's Email",
                    "type": "string"
                "Sender's Name": {
                    "description": "The name of the email sender",
                    "title": "Sender's Name",
                    "type": "string"
                "Sender's Phone": {
                    "description": "The phone number of the sender",
                    "title": "Sender's Phone",
                    "type": "string"
            "required": [
                "Sender's Name",
                "Sender's Email",
                "Sender's Phone"
            "title": "Sender Information",
            "type": "object"
    "required": [
        "Sender Information",
        "Recipient Information",
        "Email Details"

“Email Details”: {
“Date”: “<current_date>”, // You can replace this with the actual date when the email was sent
“Subject”: “Request for New Blog Posts on Large Language Models”
“Recipient Information”: {
“Recipient’s Name”: “Philip”
“Sender Information”: {
“Sender’s Email”: “”,
“Sender’s Name”: “John Smith”,
“Sender’s Phone”: “(123) 456-7890”

There are several noteworthy points:

  1. The model accurately extracted the sender’s information, recipient, and subject from the text.

  2. The model also correctly identified that the requested information, “Date,” was missing from the email and, consequently, left that field empty.

  3. This approach is versatile and can be employed for a wide range of information extraction tasks from text.

Feel free to copy the example prompt and test it with ChatGPT yourself!

Key Points:

  1. LLMs excel at understanding context and processing large amounts of text.

  2. LLMs can assist with a multitude of tasks, including information extraction, meeting minute generation, email writing, content rephrasing and paraphrasing, report and document summarization, content moderation, and more.

  3. The solutions can be implemented with any on-premise LLM or LLM service provider such as OpenAI or AWS Bedrock

Application Areas

The applications are boundless, spanning across all industries and many departments. From marketing to legal, LLMs can enhance operations and streamline processes.

Technologies, Frameworks & Tools

  1. Prompt Engineering: Crafting the right prompts for LLMs is crucial for achieving desired results.

  2. Open-source LLMs: These are readily available for customization and deployment.

  3. LLM Services: Leading services like OpenAI and AWS Bedrock offer powerful pre-trained models.

  4. Langchain for prompt workflows: Utilize Langchain to create efficient and effective LLM workflows.


By harnessing the capabilities of LLMs, businesses and professionals can streamline numerous tasks, improve productivity, enhance communication, and reduce the potential for errors. The examples provided in this article demonstrate the versatility and effectiveness of LLMs in providing assistance across a wide range of business-related challenges. Embracing this technology can propel your organization forward in the ever-evolving landscape of the modern business world.

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