In essence, these are systems that “understand” the language, as before, only people could. However, don’t be afraid: LLM doesn’t replace valued specialists. 

LLM development services enable businesses to automate the processing of vast amounts of unstructured text: from extracting key insights to generating high-quality content based on specific context.

Core principles of LLMs

As you know, an LLM isn’t born ready to work in your business; it needs to be taught to understand the context of your industry. Usually, the model is already ready to take on, and it has been trained. The process consists of 2 steps, and each is critical to getting real benefit:

– Pre-learning: the model absorbs terabytes of text from the internet: articles, forums, code on GitHub, and scientific publications. On the output, it knows how to build sentences and catch common language patterns, but still remains a “dilettante” in the specifics of your business.

– Fine-tuning: taking the basic model and training on data that matters to your company.

LLM works not as an expert, but as a probability machine that predicts the next word based on patterns rather than real understanding. The applications of LLMs create 3 key problems that are important to consider when implementing:

Hallucinations: the model can confidently name a non-existent article of law or fabricate data if its learning sample didn’t have an exact answer. Solution: Retrieval-Augmented Generation (RAG). This is an architecture where the LLM usage first turns to your knowledge base (documents, databases), finds relevant facts, and only then formulates the answer. So the model is based on verified sources, not on “memory”.

Quality control: if LLM generates contracts, letters, or medical reports, the error price is high. One solution – human-in-the-loop: the model offers a draft, man checks critical moments. Other – automatic checks: validation of the document structure, check with cheque-sheets, checking of tonality for client communications. For example, you can set a filter that blocks the sending of mail if the model used too categorical wording.

Privacy: When you send a request to a public API (such as ChatGPT), your data passes through the provider’s servers. For banks, medical institutions, or companies with an NDA, this is not acceptable. There are 2 ways:

Enterprise cloud solutions (Azure OpenAI, AWS Bedrock) – data is processed in an isolated environment with SLA security. Local models (Llama 3, Mistral) deploy on your servers. Full control, but requires more resources to support infrastructure.

E-commerce and retail

Personalized product recommendations

In retail, there’s one important thing: understand the buyer faster than he knows what he wants. 

Retail analytics solutions analyze purchase history, search queries, time of visits, and even feedback to offer what you really need.

Large language model use cases, such as ASOS and Zalando, utilize similar algorithms to match the assortment to a specific customer: the system “learns” from millions of interactions and over time predicts preferences more accurately.

Voice and text AI assistant

LLM models become the engine of smart assistants who can help with size selection or track orders.

It’s a full-service that provides context-aware product advice based on conversational history. For more information on these solutions, see chatbot development services.

Smart inventory management and demand forecasting

The LLMs analyze thousands of customer reviews and social media mentions to identify emerging trends and sentiment shifts that traditional numbers-based tools might miss. Walmart and H&M are already using similar models to understand in advance where demand will grow and optimize logistics. It has helped them achieve lower storage costs and more accurate procurement planning.

Finance and banking

The financial industry requires high precision and control. This is where fintech AI development services show how you can automate analysis without losing security.

Security and handling of confidential data

Banks and fintech companies are increasingly using LLMs that excel at summarizing complex regulatory documents and extracting key financial indicators from quarterly reports, reducing the time spent on manual research.

Automation of client requests

The flow of applications for cards, transfers, and credits can be relieved with LLM use cases.

Revolut has implemented a system where an AI model processes standard user questions and redirects complex cases to operators, which has accelerated response times and reduced the load on the support department.

Healthcare

Medicine is a place where every minute of paperwork is a minute stolen from the patient. Doctors drown in documentation, and the LLM use cases help them to return to what they went into the profession for.

Medical transcription and clinical documentation

The doctor attends, dictates notes in the microphone, or just talks to the patient, and the LLM listens and automates, turning it into a structured medical record. 

Now the model itself pulls out the symptoms, the diagnoses, and the prescriptions from the audio recording of the reception and puts everything in order. The doctor only checks and confirms.  If the doctor says, “A patient complains of a headache, probably migraine, I prescribe X medication twice a day”, the model automatically fills in the right fields: complaints, preliminary diagnosis, prescription.

With the help of OCR scanning services, the clinic has already automated the processing of tons of handwritten or scanned documents.

Predictive models for patient outcomes

LLM doesn’t make a diagnosis, but can notice patterns that slip away from the person.

When a patient is in the hospital after surgery, nurses record every day their scores, complaints, and test results. The LLM analyzes these records and notes what deviations from the norm are at this moment. Without an LLM, this could only be noticed after a couple of days, when it is already more difficult to treat.

The technology also processes thousands of patient histories with a similar cancer type. When a new patient arrives, the model looks at his or her performance (age, stage, type of tumor, associated diseases) and gives the doctor the necessary information with treatment plans.

Of course, this doesn’t replace the doctor’s decision, but gives him an additional data point for choosing a treatment tactic.

Legal

Automated contract review and analysis

Read the contract for 50 pages, where 90% – not much needed information about the script, and 10% – critical points that should not be missed – is hell, agree. 

For example, the company signs a contract with a new supplier. The lawyer uploads the document to the LLM system. The model immediately finds: “Item 12.4 – penalty for late payment 5% per day (this is higher than the standard 1-2%). Paragraph 18 – arbitration only in the jurisdiction of the supplier (usually a chosen neutral venue). Paragraph 23 – Automatic contract renewal if not notified within 90 days”. 

LLMs perform semantic cross-checks between different documents, identifying inconsistent terminology or missing clauses that might lead to legal risks.

AI-powered legal research and case summaries

The lawyer needs to find precedents in the case. And if earlier it was necessary to go into judicial databases, read dozens of cases, and look for analogies, now LLM does it for him.

If a lawyer is preparing for an appeal, the LLM can see the decision of the court of first instance and find contradictions in the reasoning of the judge, referring to similar cases where the appeal was won. Yes, it is not a guarantee of victory, but a serious saving in preparation time.

Media & entertainment

Content is the currency of the industry. However, you need to create it quickly, personalize it for each viewer, and understand what people will get. As you may have guessed, LLM works fine here. 

Personalized viewing and listening recommendations

Now, the section “You may like it”, this isn’t about “you watched comedies, here’s another comedy”. With the LLM platform, you can understand why you got what you were watching.

Let’s say you watched “Into the Grave”. A normal referral system will suggest other crime dramas. The model will recommend “Better call Saul” (the same universe, similar dynamics of the character) and… “Heirs” (quite a different genre, but there is also about moral fall and family dramas). 

Audience insights and sentiment tracking

Studios and producers want to know: what do people think about the new film/series/album? Not just “likes/dislikes”, but why they like it or not.

LLM collects feedback from Reddit, Twitter, YouTube comments, and forums. 

Now, you will find out that 60% of fans are delighted with the new character, but 40% are unhappy that their favorite hero got little screen time. The producers see this and can adjust the plot of next season.

Education

Education is still stuck in the “one teacher – 30 students with different levels” model. The LLM helps to personalize learning so that everyone can learn at their own pace.

Automated grading and evaluation tools

Here, the LLM does the primary check, leaving the teacher time for real feedback.

For example, you have tests with open questions. Before the teacher reads each answer, evaluate subjectively. 

With new technologies, it’s possible to compare the student’s answer with the standard, check key points, and evaluate the completeness of the answer without much participation. If the student writes correctly, but in other words, the model understands it. 

The teacher checks controversial cases; the rest is already evaluated.

Manufacturing

Production is a place where the stop line per hour costs tens of thousands of dollars. Manufacturing analytics solutions help to avoid breakdowns, optimize processes, and avoid losing money. 

Workflow automation and optimization

There’s a bunch of processes in production where people do the same thing in circles: they check documents, fill out forms, write reports. LLM takes it on.

Let’s say the order came from a client. Usually, the manager reads, manually creates an order in the system, checks the availability of materials in the warehouse, coordinates with production, and sends confirmation to the customer. 

Now, other ways are possible: LLM analyzes complex logistics news and market innovations to provide the manager with a text summary of risks and forecasting purchasing plans.

Supply chain forecasting

Production depends on deliveries. If the materials are late, the line stands. If too much money is frozen in the warehouse. LLM helps to balance.

If your business is electronics manufacturing, LLM will analyze for you: historical sales data (which months peak), current orders, industry news (new iPhone comes out – means demand for components will grow), and data from suppliers (one supplier’s microchips are delayed due to logistics). 

Customer support

Support is the company’s front line. If a customer waits for three hours to receive an answer or receives a useless report, they leave and don’t return. The LLM transforms support from an “inevitable evil” into a competitive advantage.

Next-gen chatbots for real-time service

For example, the customer writes: “Ordered delivery for tomorrow, but I need to change the address”.

LLM-bot: provide human-like responses by understanding the intent behind a customer’s query. Instead of rigid templates, the model interprets the request and prepares the necessary information for a seamless resolution. 

The company reacts quickly, and the situation doesn’t escalate.

Cybersecurity

Threat detection and automated responses

Classic security systems work by the rules: “If you see this pattern, it’s an attack”. Hackers have long learned to circumvent the rules. LLM is looking for anomalies that the rules will not catch.

Let’s say, employees receive a letter allegedly from the CEO asking them to urgently release financial data. LLM analyzes the letter: sender – similar domain, but not accurate (company.com vs companu.com with Cyrillic “y”), tone of the letter – uncharacteristic urgency, please send data to personal mail (CEO never does). 

The system marks a letter as suspicious, blocks links inside, and warns recipients: “Possible phishing, don’t respond”.

Agriculture

Agriculture isn’t just about tractors and fields. It’s a huge amount of data: weather, soil condition, plant health, pests. AI in Agriculture helps farmers make decisions based on data, not intuition. 

Crop monitoring with predictive insights

The farmer used to go around the fields, looking where drought and disease. Of course, it’s so interesting, especially if you are new. Nevertheless, he shouldn’t forget the human factors, or when the eyes are already so tired that he can miss the start of a very serious problem. 

Although it’s not that bad, because now it’s decisive with use cases for LLMs + computer vision development services. 

With the help of a special drone, you can see that in the northwest corner of the field, the plant is slightly paler, and the leaves begin to curl. The model compares with the base: this is an early sign of nitrogen deficiency. The farmer would not have noticed the problem yet (the plants look normal). LLM acts as a knowledge bridge in agriculture. By processing text-based reports from field sensors and satellite data, the model synthesizes complex information into actionable summaries for farmers, explaining the ‘why’ behind specific crop issues in plain language.

Or another situation: imagine that a farmer uploads photos of tomato leaves to an app. LLM with image processing services analyzes:  On the leaves are small brown spots, and the edges become yellow. The model compares with the base of diseases: it is Phytophthora, a fungal disease. The system says, “Phytophthora, early stage. We recommend treating with a type X fungicide for two days. If not treated, the disease will spread to the entire greenhouse in a week”. 

Or imagine that the musician has released an album. LLM analyzes the comments and finds: fans praise the sound experiments, but say that the lyrics have become too abstract. The artist gets specific feedback: not “album is ok in general”, but “sounds cool, but we want more personal stories in texts”.

Operational efficiency is spending less resources on routine, finding bottlenecks before they become a problem, freeing people for tasks that require brains rather than mechanical work.

LLM has already changed the working logic of many of the above companies. 

Previously, automation was referred to as “if something”: if the customer ordered the product, send a confirmation. Now, for the context: the customer ordered the product, but he has three returns in history to offer additional advice before sending it to make sure that everything fits this time.

Decisions in a company are made based on data, not intuition. The manager used to make decisions by instinct: “It seems that customers want more of this”. 

The main thing: LLM removes what slows down the work: searching for information, routine checks, filling out forms, and sorting tasks. People stay where they need creativity, empathy, and strategic thinking. But now they have more time for this because the machine has taken over the mechanics.

Yes, you can skip an implementation of LLM for now. However, you should ask yourself: how much do you really gain if you continue to spend time and resources on routine tasks, which, in any case, “eat” the energy of your employees? LLM takes over repetitive operations, information processing, and primary data analysis, allowing people to focus on strategic thinking, creativity, and customer interaction. 

As a service company with 9+ years of experience, we have repeatedly observed that the payback of such systems often exceeds the cost of hiring additional staff who will eventually face the same limitations of human energy and attention. 

Data Science UA offers a solution that goes beyond the traditional approach. Each LLM model is designed with your industry, business processes, and objectives in mind, ensuring maximum efficiency and accuracy. Thanks to our extensive network of more than 30,000 AI and Data Science professionals, we provide access to the best talent, which guarantees high-quality and innovative solutions.