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 Auto­mat­ing Suc­cess 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 Auto­mat­ing Suc­cess 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 Auto­mat­ing Suc­cess 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 synvert'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: john.smith@email.com 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": [
                "Subject"
            ],
            "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”: “john.smith@email.com”,
“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.

Con­clu­sion

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.