How to efficiently implement LLMs in your business operations

A comprehensive guide to incorporating Large Language Models into your company for increased efficiency and business value.

GPT and other LLMs hold great promise in terms of improving efficiency, streamlining processes, and ultimately driving greater business value. According to Forbes Advisor, a staggering 97% of business owners believe that ChatGPT will benefit their businesses. Whether you’re a startup or an established enterprise, embracing LLMs is no longer a choice but a necessity in today’s rapidly evolving technological landscape. As these models continue to evolve and the range of their application broadens, the need for organizations to effectively implement and leverage their capabilities has never been more critical. But how to do it efficiently? This blog post will guide you through the four steps to efficiently implementing LLMs in your business operations.

LLMs development process in 4 steps

Step 1: Go beyond the ChatGPT hype

Before diving into implementation, it’s essential to understand what LLMs are, and to learn about their capabilities and the potential concerns surrounding them. Start by exploring their technological effectiveness, debunking myths, and understanding their role within AI transformation. This knowledge base will prepare you for the remaining steps in the LLM development process.

Today, everyone is talking about LLMs, especially ChatGPT and its breakthrough capabilities, but the field of language modeling is definitely not new, and ChatGPT is not the first model of its kind. It may come as a surprise to many, but neural networks have been used for the purpose of language comprehension for over a decade, and the game-changing idea of the Transformer was first presented in 2017. Since then, the world of natural language processing has evolved into a multitude of similar yet different approaches.

For business-oriented decision makers willing to benefit from the new technology, it is not necessary to learn all the ins and outs, but knowing more than what you can read in hype-fueled headlines or articles will give you an advantage by facilitating communication with the technical teams implementing the solution for you, as well as making your LLM-related decisions more grounded.

If you are looking for a comprehensive source of knowledge about LLMs, check out our Large Language Models guide.

Step 2: Review use cases from various industries for inspiration

The next important step is to research use cases from various industries to see how other businesses are utilizing LLMs. There are a number of business areas to analyze, which may include such LLM-based solutions as content creation, knowledge retrieval, code generation, customer support, product improvements, or operational process efficiencies. Large Language Models offer unparalleled capabilities for driving business growth and efficiency. By exploring the potential use cases, you can take steps to revolutionizing your operations and achieving a competitive edge. Integrating these groundbreaking AI tools into your company is not only an investment in the future of technology but will help you pave the way to a smarter, more efficient and customer-driven business.

Many use cases are not specific to a particular industry, but can be applied at nearly every company. If your business delivers products to the market, you definitely have detailed documentation of your technology, processes and the products themselves written down in some digital form. The larger your company is, the more documents are created and the harder it is for an individual to know them all by heart, or even to know where to look for information. To address this challenge, the deepsense.ai team developed Niffler, a GPT-based application enabling users to interactively explore and extract information from large numbers of documents. By providing a user-friendly interface, the system aims to efficiently identify relevant sections of the documents in response to user queries and construct accurate, comprehensive answers based on the extracted parts of the document. Niffler efficiently processes and navigates documents, providing answers with context and adapting to evolving user needs through the LangChain library.

LLMs can be utilized in many knowledge-intensive scenarios, both internal and customer-facing. One such example may be implementing an automated support system, in which the LLMs help the customer service employee search through the historical data for issues similar to the newly reported one and automatically propose solutions. This use case is an example of the semantic search functionality, which relies on indexing documents, or rather their numerical representations, in the form of so-called embeddings calculated by the LLM, and then finding the ones most relevant to a given input query. In fact, one can use the LLMs not only to find documents of interest, but also to formulate an answer to a question based on the contents of the knowledge base.

Knowledge retrieval is not limited to customer support, but is a functionality all your employees might benefit from, e.g. through more efficient utilization of the HR or project-related knowledge base. Another use case – a customer-facing one – would be an LLM-powered search engine or chatbot available on your website, capable of answering questions about your company, its products and services, and pointing the customer to specific sources of information.

Yet another benefit of LLMs can come in the form of improving the daily work of your employees, both non-technical (e.g., in marketing content creation) and technical folks (e.g., LLMs as coding support to increase programmers’ efficiency). LLMs can also be used to improve the analysis of customer feedback (not only by looking into product ratings but also discovering detailed insights in online reviews), or to accelerate digital communication (e.g. autocompletion or proper styling of emails).

Having a general overview of what’s possible with LLMs allows you to start thinking about your business use case and focus on finding the right starting point for your own Generative AI transformation.

Step 3: Discover potential use cases for LLMs at your company

Once you have a clear understanding of the capabilities and limitations of Large Language Models, the next step is to identify your organization’s specific business requirements. Consider what problems you are looking to solve, the goals you aim to achieve, and the benefits you hope to gain.

As you look to successfully integrate LLMs into your company’s workflow, identifying and engaging key stakeholders is a crucial step towards ensuring the effective adoption and long-term success of the technology. Stakeholders have a direct or indirect interest in the outcome of the LLM software development project, and their support is essential to generating ideas for the most important business needs that can be addressed.

As part of the discovery phase, you can conduct an internal survey to gather ideas for LLM implementation, or set up brainstorming sessions to do it in a more engaging and interactive way. With a dose of creativity, you will end up with plenty of ideas for use cases.

While you brainstorm potential use cases, consider other factors like cost, resources, return on investment, and feasibility. Establish a list of potential candidates, and then prioritize based on the value they are likely to bring to your organization. To ensure that your LLM implementation will provide the expected results, you can create an effort-impact matrix including:

• a high-level feasibility study of use cases identified for your company,
• an initial assessment of the business impact for each initiative.

The result of this exercise will be a clear vision of the most promising initiatives that can be further discussed in terms of the key benefits and risks.

Step 4: Prepare the technical background

Remember that to fully benefit from the LLM technology, it is important to make sure that you have the appropriate data to fuel the model in place, and your budget is sufficient to cover the costs of model training, deployment and maintenance.

Once you have narrowed down your list of potential use cases, it is important to run validation tests and evaluate the effectiveness of the Large Language Model from various angles. This may involve prototyping, experimenting with different prompts, and analyzing the output quality based on accuracy, comprehensibility, utility, and potential risks. It’s time to lay the technical groundwork for LLM integration, which should involve:

• selecting the appropriate model – choose the LLM with the capabilities that best suit your use case,
• preparing the data – ensure the data you provide to the model is high-quality, diverse, and relevant to the specific tasks you want the model to perform,
• planning the integration with existing systems – develop a plan to seamlessly incorporate the LLM into your current workflow and the tools used by your business,
• monitor and evaluate performance – continuously track the performance of the LLM and make adjustments as needed to improve results and avoid unintended consequences.

There are two main approaches you can follow: either simply connect your application to an API which serves a particular LLM (those from OpenAI, Google, Anthropic and Cohere are the most popular), or leverage one of the available open-source alternatives (the majority of which can be found in the Hugging Face model repository) which can be customized to your needs. Both these options have pros and cons as they differ in terms of time-to-solution, pricing, level of control over the solution, data privacy or response latency. For instance, the costs of using the API are related to the number of requests (and the length of included text prompts) sent to the model. If you prefer serving the LLM on your premises or in your private cloud, make sure to equip your infrastructure with powerful hardware.

With the rapid expansion of Generative AI, the technology stack behind it also continues to grow with multiple options to choose from. Tools like LangChain or various vector databases (Pinecone, Chroma, Weaviate to name a few) are becoming increasingly popular as they allow you to quickly develop not only prototypes but entire applications powered by LLM, shortening the path from an idea to a working solution.

You should also remember that the process doesn’t stop after the deployment of the first solution. Continuously refining and iterating on the use case throughout its lifecycle is crucial to maximizing its effectiveness. This may involve incorporating advanced functionalities, fine-tuning the model’s performance, and taking user feedback into consideration. Having the MLOps best practices (referred to as LLMOps in the context of LLMs) implemented, including model monitoring, retraining, reproducibility and versioning, is becoming not only a nice-to-have feature but rather a necessity if you want to have the job done properly.

LLM development: final thoughts

Efficiently implementing Large Language Models in your business operations requires thorough research, strategic planning, and the right technical infrastructure. A deepsense.ai GPT and other LLMs fast track workshop might be the missing piece of your success puzzle. You can easily delve into the world of LLMs and unleash their potential with the guidance of deepsense.ai’s AI experts. Our customized fast track workshop will grant you insight into the nuances of LLM technology, providing you with a solid understanding of the latest breakthroughs. But most importantly, we equip you with the necessary knowledge to explore practical and implementable use cases within your industry and chart out steps for your business growth.