How to Build an LLM? Definition, Use Cases, and Steps

• AI/ML
• October 24, 2025

by Akash Patel

Developing your enterprise-grade AI solution and need to make it live faster? Let’s opt for ready-to-integrate, general-purpose LLMs! But wait, are you ready to tackle challenges generic LLMs create, like hallucinations, logical reasoning failures, data security and privacy vulnerabilities, outdated or generalized knowledge, etc.? Off-the-shelf, ready-to-integrate LLMs like ChatGPT may seem cost-effective and time-saving, but they do incur cost implications later on.

In such cases, developing a proprietary Large Language Model (LLM) can offer significant advantages.

Custom LLM development services enable the use of proprietary datasets, the fine-tuning of outputs for specific use cases, and the delivery of AI solutions that are both precise and aligned with business objectives.

Whether it’s automating customer support, generating insights from complex documents, or enhancing internal knowledge management, a well-built LLM can transform operations and decision-making.

This guide breaks down everything you need to know about how to develop LLMs, from understanding the different types and their business applications to the step-by-step process. By the end, you’ll have a clear roadmap for implementing enterprise-ready AI that drives tangible value.

KEY TAKEAWAYS

• Off-the-shelf models are powerful, but a domain-specific LLM can deliver more accurate, context-aware, and compliant AI solutions tailored to your business.
• To build your own LLM, you should start by defining the use case, audience, and success metric as part of your strategic groundwork.
• The smarter the data, the smarter the LLM, so clean, curated, and well-structured datasets are non-negotiable.
• Fine-tuning is usually the fastest path to improve the performance of LLMs.
• Continuous human feedback is critical to train LLMs for accuracy, safety, and relevance.

So, are you interested in building your own LLM that’s trained on your datasets and delivers results as you want? Hire AI developers from MindInventory!

What is LLM?

LLM stands for Large Language Models, a type of artificial intelligence that leverages AI/ML models to understand, interpret, and interact in human language. At its core, an LLM is a neural network, often based on the Transformer architecture, that learns patterns, context, and relationships in text.

So, you can say an LLM can act as a scalable knowledge worker, automating repetitive tasks, improving decision-making speed, and providing insights from vast amounts of unstructured text.

Top examples of LLMs include GPT-4 by OpenAI, Gemini by Google DeepMind, Claude 3 by Anthropic, LLaMA 3 by Meta, and Mistral 7B by Mistral AI.

Types of LLMs Businesses Can Build

Large language models (LLMs) are categorized by architecture, training data and purpose, and modality types, if following academic values. The main types of LLMs include decoder-only, encoder-only, encoder-decoder, and multimodal models, each designed for specific natural language processing tasks and applications.

If you’re a business looking for practical types of LLMs, then you should consider general-purpose, open-source, domain-specific, or proprietary models.

Let’s get an overview of practical types of LLMs businesses should know about:

1. General-Purpose Foundational Models

These are large, pre-trained base models designed to handle a wide variety of tasks without needing to be rebuilt from scratch. They form the backbone of many AI/ML applications by providing versatile reasoning, comprehension, and text generation capabilities.

Examples: Gemini, GPT, Claude, and PaLM.

2. Open-Source Models

These models are publicly accessible, allowing ML developers and organizations to inspect, modify, and retrain them for specific needs, often at a lower cost. They are often smaller or community-supported variants of commercial LLMs but encourage innovation and transparency.

Examples: LLaMA 3, Mistral 7B, Falcon, and RedPajama.

3. Domain-Specific/Custom-Trained Models

This type of LLM is trained on specialized datasets for a particular industry, such as healthcare, finance, legal, etc. They provide higher accuracy in niche contexts where industry terminology and compliance are vital. Hence, they are useful for tasks that require specialized knowledge, compliance phrasing, or a deep understanding of a specific field.

Examples: BloombergGPT (finance), Med‑PaLM 2 (healthcare), and LegalBERT (legal).​

4. Proprietary Models

As the name suggests, these models are developed and owned by specific companies. Proprietary LLM models are offered as closed-source services, which are accessible mainly through APIs. They are trained on private, often curated datasets and fine-tuned for high accuracy and compliance with industry standards.

These models provide state-of-the-art performance, reliable enterprise-grade support, regular updates, and seamless integration into existing business systems, which makes them ideal for organizations focused on stability and ease of deployment.

Examples: Claude 3.5 Sonnet and Gemini 1.5 Pro.

Why Generic LLMs Fall Short in Enterprise Environments

Generic large language models (LLMs) fall short in enterprise environments primarily due to limitations in handling complex, large-scale, and domain-specific tasks that enterprises require.

Apart from that, key reasons include their:

• Limitation in processing the amount of text at a time.
• Lack of understanding of a company’s unique data, terminology, and processes.
• Lacking important features like prompt governance, access control, and data isolation, they need to meet enterprise-grade security requirements.
• “Black box” nature prevents them from ensuring compliance, tracking decisions, or verifying the accuracy of outputs.
• Disability to reliably interact with multiple enterprise systems to complete tasks.
• Lack of structured reasoning to execute precise business processes.
• Proneness to generating inaccurate or “hallucinated” content.
• Weak confidentiality controls and compliance awareness.
• Performance can be inconsistent and lacking the stability, predictability, and version control.

Why Should You Build Your Own LLM?

You should build your own LLM for greater control over data security, model customization, and performance for specific needs.

Some of the key reasons to build your own LLM include:

• Trust assurance, ethical alignment, and bias control, ensuring your model operates within your organization’s ethical, legal, and compliance boundaries.
• Data security and privacy management, as your own model won’t rely on external servers, reducing the risk of data breaches and ensuring compliance.
• Customization and specificity control allow you to train LLMs with your own data, leading to more accurate and relevant outputs for niche use cases.
• Full control and independence, enabling you to align the LLM with your specific requirements and business goals (covering the model’s architecture, training data, and updates).
• Competitive advantage, as you’ll be able to create unique services, products, or customer experiences, differentiating you from competitors.
• Potential cost savings at scale and in the long run, if you have a high volume of inference requests.
• Explainability is another key consideration for building your own LLM as it helps to maintain end-to-end transparency and control, enhance reliability in high-stakes domains, and ensure data privacy and compliance.

Should You Train Your Own LLM or Use an Existing One?

You should use an existing LLM if you need to deploy quickly, as it’s more cost-effective and requires less technical expertise, while you should train your own LLM if you need domain-specific knowledge, high data privacy, or custom reasoning that existing models lack.

Let’s have a look at a quick comparison table to know whether to train your own LLM or use an existing one:

A Step-By-Step Process to Build Your Own LLM

Developing a large language model involves multiple structured stages, from defining the purpose and curating datasets to training, deployment, and continuous improvement. Below is a step-by-step overview synthesizing a process to build your own LLM:

Step 1: Define Objectives & Strategy

Before you start developing a large language model, clarify what your LLM will do, who will use it, and how you’ll measure its success.

Also, consider the type of solution you want to build; for example, a conversational assistant for internal support teams, an automated summarization engine for quick document review, or a code generator for developers.

Once you decide the purpose, set measurable KPIs early. Focus on accuracy, response latency, cost per query, and factual reliability. These KPIs will help you align business goals with technical direction.

Next, decide on the approach that helps you build your own LLM. You can decide to either:

• Fine-tune an existing LLM, like LLaMA, Mistral, or Falcon, to your specific domain. Many enterprises think of this, as it is the most cost-efficient, reliable, and fast-to-market approach.
• Train from scratch by building your own model from the ground up using massive datasets and compute infrastructure. It indeed offers full control but requires billions of tokens, powerful GPUs, and months of experimentation.
• Use an RAG-based system, which allows you to combine an existing LLM with a vector database or document store to provide live, factual responses grounded in your proprietary data. It’s the ideal middle ground for organizations that need accuracy without retraining.

Step 2: Prepare & Curate Data

The effectiveness of your LLM depends on the quality of data you feed it to learn from.

So, collect data with relevance from multiple reliable sources, such as internal knowledge bases, support tickets, manuals, research papers, or web data.

Post this clean and preprocess the data by removing duplicates, irrelevant sections, and personal identifiable information (PII). Then, normalize formatting, fix encoding issues, and standardize punctuation and spacing.

You can also opt for synthetic data generation if you lack some specific data to train LLM on.

Then comes tokenization, which includes converting text into numerical tokens that the model can understand. If you’re fine-tuning the LLM, then use the tokenizer from your base model to maintain compatibility. For models trained from scratch, build a custom tokenizer (BPE or SentencePiece).

Consider this:

• Fine-tuning typically needs tens of thousands to a few million high-quality samples.
• Training from scratch may require hundreds of billions of tokens and extensive computing resources.

The higher quality and more unbiased data you use to train your LLM, the better and more reliable performance you can expect from your LLM.

Step 3: Model Development & Training

Once your data is ready, move to model development. Start by selecting the model architecture, like whether you want your LLM to be based on:

• Transformer-based architecture (e.g., decoder-only models, encoder-only models, or encoder-decoder models)
• Recurrent Neural Networks (RNNs)
• Mixture of Experts (MoE)
• Mamba (State-Space Models)
• Convolutional Neural Networks (CNNs)

The choice of your LLM architecture should totally depend on your computing budget and intended scale.

Post this, and decide on a training strategy from:

• Fine-tuning, where you refine an existing model using your dataset to specialize it for domain-specific language or tasks.
• Instruction tuning, where you train the model to understand and follow human-like instructions, making it more conversational and context-aware.
• Reinforcement Learning from Human or AI Feedback, where you align the model with preferred behaviors, such as helpfulness, factuality, or safety, using feedback loops.

With data, a model, and a strategy set, you can move to active training, where:

• You’ll use frameworks like PyTorch, Hugging Face Transformers, DeepSpeed, or PEFT to configure training and tune hyperparameters, such as batch size, learning rate, optimizer type, and gradient accumulation for stability.
• Throughout training, continuously validate and evaluate model outputs using both automated metrics (loss, perplexity, accuracy) and human evaluations for quality, factual accuracy, and bias detection.

This ensures the model learns effectively and aligns with intended outcomes.

Step 4: Fine-tune and Optimize the LLM

Post-training, align the model’s tone and responsiveness.

Adjust prompt formatting or instruction weights, or apply an additional supervised fine-tune for conversational behavior.

This ensures the LLM aligns with brand tone, compliance guidelines, and domain nuances.

Human feedback loops (via rated responses) can also enhance output quality over time.

Not just that, you also want to make the LLM efficient for inference. You can do that by applying techniques like:

• Quantization: Reduces precision (e.g., 8-bit or 4-bit) to lower latency and memory use.
• Pruning: Removes redundant weights without major accuracy loss.
• Distillation: Transfers knowledge to a smaller, faster model.

This process ensures that LLM is deployable at scale while ensuring responsiveness and cost efficiency.

Step 5: Deployment & Continuous Improvement

Next, set up deployment via secure APIs or microservices, integrating observability, rate limits, and logging for real-time performance tracking.

If the use case requires contextual, factual responses, integrate a RAG (Retrieval-Augmented Generation) layer. This connects the model to external knowledge bases or vector databases, enabling it to reference live or domain-specific data.

Finally, establish a feedback and monitoring loop, which helps you track user interactions, identify hallucinations, and retrain periodically with new or improved data.

Based on the data you gather, retrain your LLM periodically or fine-tune it based on new examples. This ongoing optimization ensures your LLM stays aligned with business goals, domain evolution, and user expectations.

Want to Build Your Own LLM? Here’s Why You Should Choose MindInventory!

Building an LLM requires a combination of technical skills in machine learning, deep learning, natural language processing, and data science. Hiring experts in each of these AI fields as your in-house team can require too much investment.

As the best solution, we at MindInventory come in. We bring together AI expertise, engineering precision, and domain understanding to help enterprises design, train, and deploy LLMs that follow their objectives.

Whether you want to fine-tune an existing foundation model, develop a custom domain-specific LLM, or integrate RAG for real-time context, our team delivers full-cycle AI solutions.

Our clients trust us for our

• Consultative approach, helping them deeply align AI strategy with domain-specific needs.
• Multimodal capabilities to integrate text, voice, image, and tabular data to build a versatile LLM solution.
• Accelerated time-to-value we bring through reusable modules, synthetic data generation, and more, supporting fast project turnaround.
• Expertise in LLMOps and lifecycle management, covering automation, scaling, monitoring, and retraining to improve it over time.

Choosing MindInventory gives your business access to domain experts, full-stack AI engineering, and a collaborative partnership to ensure your custom LLM brings tangible impact and competitive advantage.

Frequently Asked Questions