With the recent surge in applications and widespread adoption of artificial intelligence (AI), large language models (LLMs) have become very popular for businesses, developers, and non-developers, as they provide numerous user benefits. Developers help debug code and generate code snippets, and they also help clarify seemingly complex programming concepts and tasks, leading to a boost in productivity.
Non-developers who utilize LLMs benefit from swift and tailored responses and answers to questions as long as the dataset used for its training covers the subject matter.
What are LLMs?
Large Language Models (LLMs) are AI models capable of understanding and generating texts, among other tasks. They are trained on huge data sets, so they are called “large language models.”
The transformer algorithm is the main algorithm used in training LLMs.
LLMs heavily rely on data; the quality of the model is directly proportional to the quality of the data used to train it.
Applications of LLMs in the Real World
LLMs are widely utilized for various applications, including but not limited to:
- Customer Support Services:
LLMs are used by businesses to provide tailored responses to customers’ frequently asked questions in a natural and fluid way. Due to their multilingual capabilities, LLMs can understand and respond to queries in different languages, which can benefit customers of different nationalities - Content Generation:
LLMs have generative capabilities. They can generate textual content based on user prompts or inputs, such as poems, business proposals, emails, etc. However, some of the content generated can be factually incorrect, outdated, or unethical. It is always advisable to validate the content generated by LLMs - Sentiment Analysis:
LLMs can analyze the emotion and tone of texts. Businesses use this ability in several ways. For example, they run sentiment analysis on customer feedback to determine the level of satisfaction - Study Assistance:
LLMs can help users study a new course or concept, generate learning roadmaps, and explain low-level concepts whenever they face challenges - Healthcare:
LLMs are used for health care administration, health personnel support, research purposes, and patients’ virtual assistance in the absence of physical medical personnel. These LLMs used in the healthcare sector are trained on large validated medical datasets, literature, and even emerging findings - Search Engines:
Traditional search engines were based on keyword algorithms, which made them slightly ineffective in handling typo errors and understanding human language, context, and intent as an LLM would. However, that has changed as LLMs are currently being used in search engines, making them capable of returning search results more effectively
Ways to Integrate LLMs in an Existing Software System
Clarity of purpose is key to successfully integrating an LLM into an existing software system; this way, the right approach is implemented to solve the challenge without causing excess overhead.
Listed below are the recommended steps to integrate LLMs into existing software systems:
1. Choosing the right LLM or API Provider
Depending on business needs, the technical team’s skillset, time constraints, and familiarity with certain technologies, a suitable pre-trained model can be chosen for integration. It is also important to note that factors like the accuracy of the model, model latency, scalability, and costs of running the model should also be taken into account when choosing the model to integrate into your project.
Listed below are some available options and links to access them:
- OpenAI API provides access to GPT-3 and GPT-4 models
- Azure Cognitive Services
- Facebook’s LLaMA
- Google’s BERT
- Hugging Face
- Custom Trained LLM(this can be achieved by using frameworks such as PyTorch, TensorFlow, or Hugging Face’s Transformers to train your models on available datasets
2. Selecting the Desired Integration Mechanism
LLMs can be integrated through API calls to publicly available models, hosted fine-tuned models, or whichever meets your demands. Integrating through API calls to public models is always the easier option.
You only have to worry about the cost of obtaining access through API keys, and you don’t have to worry about the infrastructure or software updates; the LLM API provider takes care of all of that for you.
Hosting custom LLMs allows you to fine-tune and utilize a model that meets specific business needs. Hosting your custom model gives you the charge over the cost and performance, and it also provides privacy when compared to the public API service.
In hosting a custom model, you need to do the following:
- Choose and download a publicly available or open-source pre-trained LLM such as GPT2, GPT3, BERT, or even T5 from a repository like Hugging Face
- Fine-tune the model based on your specific dataset to align with your use case or application, this way, responses are tailored to a particular domain, making the model more efficient(optional step)
3. Setting up the Infrastructure
This step is necessary if you have decided to host your custom models by yourself instead of utilizing a Public API Service. Hosting LLMs requires hardware with high computing power, so hardware with GPUs is preferred for better performance.
The two options available for hosting your LLM are either:
- Self-hosting(on-premise) or
- Cloud hosting
4. Serving the Model
Flask or FastApi are some of the most commonly used backend frameworks for connecting to and serving LLM models. You can use either of them to interact with your LLM and return a response.
You can use the cloud hosting option if you don’t want to handle hardware purchases, cooling, maintenance, software upgrades, and other related tasks. This involves utilizing cloud GPU-powered virtual machines for deploying your LLMs. Cloud providers such as Azure, AWS, or Google Cloud provide good service.
5. Processing/Handling of Data
Ensure that the data sent to the LLM is properly cleaned and tokenized for better understanding and quality response from the model.
6. Error Handling
Set up mechanisms that allow for “graceful degradation”. Whenever errors occur, a default response can be returned instead of letting the entire application crash.
7. Security/Privacy considerations
The model mustn’t expose sensitive information while making inferences, so measures must be taken to ensure the obscurity of vital information.
Cons of Integrating LLMs in a Software System
Despite the many advantages of integrating LLMs into existing software, there are also some downsides that are worth noting and considering.
- Computational costs of training models and during model inferences due to heavy reliance on high-end GPUs and TPUs
- Making frequent API calls can be expensive, especially for high-traffic applications
- If sensitive data is sent to an LLM, it might be processed, stored, and exposed to another user, especially when the LLM being utilized is accessible to the public
- Aside from properly fine-tuned custom models, most LLMs can only provide open-ended and generic responses and cannot provide domain-based knowledge
- Training LLMs require high energy usage, which can lead to high carbon emissions and environmental pollution
Conclusion
Integrating LLMs into an existing software system can be very rewarding if done correctly, as many renowned tech companies, such as Meta, Google, and X, to mention but a few, have done. It makes software intelligent and capable of solving complex contemporary tasks.
Before integrating your existing software with an LLM, you must first define the problem you want it to solve and the use case, after which the right model is selected and the integration mechanism adopted.
Shittu Olumide is a software engineer and technical writer passionate about leveraging cutting-edge technologies to craft compelling narratives, with a keen eye for detail and a knack for simplifying complex concepts. You can also find Shittu on Twitter.