How to create a custom LLM AI chatbot over your Company’s data

What is an LLM, and why is it essential for businesses?

Language models are artificial intelligence systems that can understand and generate human-like text. An LLM (Large Language Model) is a type of language model that leverages machine learning algorithms to process and generate natural language texts. Sometimes referred to as Generative AI, these models are designed to understand and respond to textual input in a conversational manner using a chatbot, making them an ideal tool for building internal AI-powered solutions.

Definition of LLM

An LLM, short for Large Language Model, is an AI model that can understand and generate human-like text based on a given prompt or query. It uses advanced machine learning algorithms to analyze vast amounts of data and learn patterns in language, enabling it to generate coherent and contextually relevant responses.

Importance of LLM for businesses

LLMs have become increasingly crucial for businesses as they offer a more advanced and intelligent alternative to traditional chatbots. With their ability to understand natural language inputs and provide meaningful responses, LLMs can significantly enhance customer support, knowledge base management, and data analysis processes.

Benefits of using an LLM over traditional chatbots

Unlike traditional chatbots that rely on pre-defined rules and responses, LLMs can generate dynamic, contextually relevant replies by leveraging their vast knowledge base. This flexibility allows LLMs to handle a wider range of queries and provide more accurate and personalized responses to users.

Steps to build an LLM for your company’s data
(DIY Model)

Step 1: Gathering and organizing data

The first step in building an LLM is to gather and organize a suitable set of data. This assembled data should be relevant to the target knowledge domain of the LLM and include a variety of texts that could consist of such documents as customer inquiries, product information, company policies, and support documentation. The gathered data should be diverse and representative of the language and topics you expect the LLM to handle.

Step 2: Choosing the right LLM framework

Once you have your dataset, the next step is to choose the proper LLM framework. There are numerous LLM frameworks available, such as ChatGPT (GPT = Generative Pre-trained Transformer) from OpenAI, LLaMA from Meta (which is open-source), or Claude from Anthropic.  These frameworks provide pre-trained LLM models that can be fine-tuned on your specific internal company data.

Step 3: Preparing the data for training

Before training the LLM, it is essential to identify your data source(s) and to preprocess and clean the dataset. This may involve removing irrelevant or duplicate data, eliminating any bias, tokenizing the text into smaller units, and formatting the data in a way that is compatible with the chosen LLM framework. This step ensures that the LLM receives high-quality input during the training process.

Training and fine-tuning the LLM

Training the LLM with your company’s data

Once the dataset is prepared, it can be used to train the LLM. The training process involves feeding the dataset to the LLM model and adjusting its internal parameters to minimize the difference between the generated output and the desired output. Utilizing an AI development platform like Langchain for document embedding and indexing combined with a vector database like Pinecone for efficient storage and retrieval greatly expedites training and enhances model performance.  The training process can take several hours or even days, depending on the size of the dataset and the complexity of the LLM model.

Fine-tuning the LLM for specific use cases

After the initial training, it is advisable to fine-tune the LLM for specific use cases or domains. This involves further training the LLM on a smaller, domain-specific dataset to improve its performance in handling specific types of queries or tasks. Fine-tuning helps the LLM become more accurate and reliable in generating relevant responses.

Dealing with challenges during the training process

During the training process, you may encounter challenges such as overfitting, where the LLM gets too focused on the specifics of the data it was trained on and is unable to apply its knowledge to new data. To mitigate this, techniques like regularization and early stopping can be used to help prevent overfitting issues and improve the LLM’s ability to handle a broader range of inputs.

Integrating the LLM into your business

Integrating the LLM into your chatbot platform

Once the LLM is trained and fine-tuned, it can be integrated into your existing chatbot platform. This typically involves using the LLM’s API (Application Programming Interface) to send user queries and receive the LLM responses in real-time. The integration process may require an API key or other credentials, depending on the LLM framework you are using.

Customizing the responses of the LLM bot 

To ensure the LLM provides relevant and accurate responses, it is crucial to customize its behavior according to your business requirements. This can be done by modifying the prompts or queries used to trigger the LLM’s response generation (known as prompt engineering) and by defining rules or post-processing steps to filter or modify the generated outputs.

Testing and optimizing the LLM’s performance

After integration, it is vital to thoroughly test the LLM’s performance to ensure its accuracy and reliability. Testing can involve using predefined test cases, real user interactions, or simulated scenarios. Based on the test results, optimizations can be made to fine-tune the LLM further and improve its performance in real-world scenarios.

Steps to deploy an LLM for your company’s data
(DIFM Model)

At LakeTurn Automation, we take the hard work (see above) and extended timeline out of deploying an LLM over your internal data.  Our pre-configured LLM infrastructure and toolsets enable a rapid chatbot deployment over your internal data using our proven 6-step implementation process.

  1. Define LLM Business Objectives  Define the business use case and requirements for the LLM chatbot, determine the availability and quality of data required to support the business objective, and assess access and security requirements.
  2. Evaluate Data Quality and Compatibility  Assess data availability, format, and quality required to support the business objective. Determine data clean-up requirements and assignments if/as needed.  
  3. Configure the ChatBot  Complete chatbot configuration (ex., security, persona, greeting, prompt, creativity, suggested questions, scheme, colors, avatar, etc.).
  4. Complete ChatGPT Setup Determine proper ChatGPT model requirements (i.e., 3.5, 3.5-16K, or 4) and set up OpenAI API key. 
  5. Load Data Sources and Train Upload all identified and cleansed internal data into the model and execute the training process for each document uploaded.
  6. Test and Deploy Utilizing test questions, evaluate chatbot responses to ensure they are consistent with the trained data elements.  Tune the chatbot prompt, augment data, train, and repeat (if/as required) until satisfied with the chatbot responses. 

Potential use cases and benefits of an LLM for businesses

Customer support and service

An LLM can significantly enhance customer support processes by providing instant responses to customer queries, resolving common issues, and offering personalized recommendations. This can lead to improved customer satisfaction, reduced response times, and increased operational efficiency.

Knowledge base management

LLMs can be used to build a custom knowledge management system that can automatically answer frequently asked questions, provide product details, and assist employees in finding relevant customer, product, or company policy information. This simplifies knowledge management and ensures consistent and accurate information dissemination within the organization.

Data analysis and decision-making

LLMs can be leveraged for data analysis tasks, such as sentiment analysis, trend identification, or summarizing large volumes of text. By analyzing and extracting meaningful insights from internal textual data, LLMs can assist businesses in making informed decisions and identifying patterns and trends that might otherwise go unnoticed.


In conclusion, deploying an LLM for your company’s data can bring numerous benefits to your business. By leveraging advanced AI technologies, an LLM can revolutionize customer support, knowledge base management, and data analysis processes – for customers and employees alike.  Follow the steps outlined in this guide to implement a custom AI chatbot powered by an LLM, and unlock the full potential of your company’s data to enhance productivity and improve stakeholder satisfaction. 

Ready to rapidly unlock the untapped potential of your company’s data? Contact us today to learn more about our turn-key LLM services.