The biggest challenges with LLMs, and how to solve them

Large language models (LLMs) are the product of the rapid developments in AI, and they’re now widely used in tools like chatbots, content generators, and code completion systems. But as these models become more widespread, businesses are facing significant challenges ranging from LLM development and integration to the adoption of LLM-based tools in daily operations. Let’s explore the different types of LLM challenges and potential solutions, including how AI orchestration platforms can help your organization manage multiple language models.

Types of LLM challenges

If developing large language models or using them is part of your business strategy, you should be aware of the difficulties your company might face. LLM challenges can roughly be divided into three types — issues arising while developing LLMs, problems with LLM tools, and difficulties in applying LLMs in everyday tasks.

Challenges in the development of LLMs

Any business that chooses to build and train its own natural language processing model will encounter significant challenges, primarily related to resources and expertise.

• Data quality and bias. If your company is developing an LLM, you must ensure the vast datasets fed into the model during training are accurate, diverse, and free from bias. This means you have to carefully select and curate input data so that it reflects a wide range of perspectives and is free from harmful stereotypes.

• Computational demands. Training LLMs takes a lot of substantial computational resources and memory, which can get expensive. The costs mainly come from high energy use and the need for powerful hardware like GPUs or TPUs.

• Model complexity. LLMs have complex architectures with many layers of neural networks and hyperparameters that need careful tuning. You’ll need technical expertise to make sure the model is effective and efficient, avoids overfitting, and performs well on different tasks.

• Ethical and regulatory compliance. Businesses must follow privacy laws when training LLMs, especially with personal data. This includes secure handling and compliance with the General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA) in the US to protect both users and companies.

Challenges with LLM tools

When integrating an LLM or an LLM-based tool into existing workflows, companies may face difficulties with compatibility, scalability, maintenance, and security.

• Tool compatibility. It can be challenging and may require adjustments to make your LLM tool compatible with existing systems and application programming interfaces (APIs).

• Scalability. LLM tools must be able to handle varying traffic loads and perform well under high demand or during traffic spikes. Without proper scalability, the system can slow down or become unresponsive, which frustrates users or leads them to choose a different tool.

• Monitoring and maintenance. You need to spot errors, fix performance problems, and make adjustments regularly to keep your LLM tool running well.

• Security measures. You need to safeguard LLM tools from unauthorized access and keep model endpoints safe from potential threats to maintain data privacy and security.

Challenges with the application of LLMs and their everyday use

Real-world deployment of your large language models and LLM-based tools present their own set of challenges related to hallucinations, transparency, staying current, and ethics.

• Hallucinations and inaccuracies. Sometimes LLMs generate outputs that sound convincing but are actually false or misleading (known as “hallucinations”). The developer must implement safeguards that help identify and correct inaccurate responses.

• Trust and transparency. The success of an LLM in the real world largely depends on whether users trust it or not. Unrealistic expectations lead to frustration and disappointment, so if you want people to use your large language model, you must be transparent about what it can do and where it might fall short.

• Keeping models up to date. A major challenge is ensuring LLMs can quickly adapt to new data, trends, or changing user needs. Without regular updates, the models can become outdated, which leads to less accurate responses.

• Ethical considerations. The application of LLMs also presents ethical challenges. It’s crucial to avoid harmful or biased outputs and maintain accountability, especially when models make automated decisions that can impact users or businesses.

6 biggest and most common LLM challenges

From the long list of challenges related to the development and application of language models and LLM-based tools, six stand out as the most common ones.

1. LLMs are not very cost efficient

Training and running LLMs require massive computing power, which drives up costs. You need clusters of GPUs or TPUs, and they consume a lot of energy. Cloud services add to the expense — on-demand usage can quickly spiral out of control if you don’t manage it carefully. For example, leaving idle instances running or scaling inefficiently can waste thousands of dollars.

And it’s not just training — serving the model in real-time also adds costs. Without careful planning, expenses can outweigh the benefits and make LLM adoption unsustainable.

2. Manual checks are impossible with large datasets

A key challenge in LLM training is that training datasets are too large to check manually. With terabytes or even petabytes of training data, ensuring accuracy at every step is nearly impossible.

Errors and biases can easily slip through, especially with diverse sources like social media or user-generated content, which often contain noise and inconsistencies. If you don’t catch these issues early, they can compound and impact the model’s accuracy and reliability.

3. Security and data privacy concerns

Security and data privacy concerns are a major challenge when working with LLMs, so it should be the top priority for LLM developers. Large language models often process sensitive or proprietary information, which means they must comply with GDPR, CCPA, and the Health Insurance Portability and Accountability Act (HIPAA).

Training data collection also raises ethical concerns. During LLM training, vast amounts of data are gathered from websites, books, and publicly available content. There have been suspicions that some LLM developers might be scraping data from the internet without proper consent, raising questions about the ownership and rights to the information used for model training.

4. Inaccuracies and hallucinations

LLMs can sometimes produce hallucinations — model outputs that sound right but are actually incorrect. Hallucinations and inaccuracies can lead to poor decisions if users rely on the wrong data, which can cause a valid distrust of AI tools. It’s a big issue in the model application stage, where users depend on accurate information.

Getting the LLM to reach a satisfactory level of accuracy requires extensive testing, including trial-and-error prompt engineering and fine-tuning based on human feedback. The best way to reduce inaccuracies and hallucinations is to combine LLMs with external databases for fact-checking, continuously fine-tune the model with corrected information, and use confidence scoring to help users assess reliability.

5. Challenges with scaling and staying current and up to date

Many organizations struggle to keep LLMs updated with the latest information. As new data constantly appears, you need to retrain your models or supplement them with real-time data to help them stay relevant and effective.

The problem is that retraining a model from scratch is time-consuming and costly, especially for large datasets. If you don’t keep the models up to date, they can spread outdated information or fail to accurately respond to new user queries, which makes them much less helpful.

6. Difficult LLM integration

Integrating LLMs with existing systems, tools, and data pipelines can be challenging. LLMs need to communicate with other software and data sources through APIs, libraries, or custom wrappers — setting up and managing these connections requires specialized expertise. If you don’t integrate it correctly, it can disrupt workflows, delay processes, or slow down systems.

For example, if the LLM doesn’t process data in the way your current tools expect, it can lead to errors or crashes. Ensuring the model works seamlessly with other systems often means adjusting and fine-tuning the connections, which can be both time-consuming and complex.

Solutions to LLM challenges

To help your business tackle the most common LLM challenges, let’s explore solutions like orchestration and automated routing, data quality automation, fine-tuning, and retrieval-augmented generation.

Solution 1: Data quality automation and monitoring

During the LLM development stage, automating data quality and monitoring helps tackle issues like large datasets, inaccuracy, bias, and data privacy. By setting up pipelines to automatically clean, label, and audit your training data, you’re making sure you’re using only accurate and relevant data to train the model.

In the training stage, it’s important to use anomaly detection tools for identifying unexpected patterns or biases. They allow you to address these issues before they affect model performance. Using privacy-preserving techniques, like anonymization, protects sensitive information and reduces the risk of privacy breaches.

In the long term, quality automation and monitoring practices lead to more reliable models. Automated checks help maintain regulatory compliance and ensure personal data is handled according to privacy laws. Additionally, feedback loops help improve the data over time.

Solution 2: Fine-tuning and retrieval-augmented generation (RAG)

Fine-tuning helps solve challenges like inaccuracies and hallucinations by continuously retraining LLMs on domain-specific data. This way, the model stays relevant and aligned with the specific needs of an industry or task. Fine-tuning also reduces errors over time.

RAG improves the model by integrating external knowledge bases that provide up-to-date, factual information. This reduces the likelihood of hallucinations because the model can pull in real facts to back up its responses.

In the long run, fine-tuning and RAG make models more reliable by helping them provide more accurate answers. These methods also let businesses customize models for specific industries, like legal or healthcare. Plus, when the model sources its information transparently, it builds trust with users and makes them feel more confident in the responses they get.

Solution 3: Orchestration and automated routing

Orchestration and automated routing can help tackle challenges like LLM inaccuracy and data privacy concerns. A centralized LLM orchestration platform like nexos.ai automatically directs tasks to the most suitable model based on the data and the task at hand, which reduces the risk of inaccuracies in the output. For example, a task that requires domain-specific expertise can be routed to a model that is more accurate for that type of content, while more general tasks can be handled by less specialized, cost-effective models.

It’s also easier to comply with privacy regulations if you’re using AI orchestration and automated routing platforms. They automatically select different models for different tasks, ensuring that sensitive data is processed with models specifically designed to meet security and privacy standards. Orchestration platforms also track where and how your data is processed, which helps reduce the risk of data breaches.

Real-world examples and application of LLMs

Let’s look into some use cases to see how LLM orchestration strategies can help to overcome LLM challenges and avoid their limitations.

Customer support

Handling a surge in user queries can quickly lead to higher costs. By using an orchestration platform, you can distribute incoming requests across multiple LLMs and cache repeated questions. This way, your business can handle common inquiries quickly and without using too much resources.

Text generation

Producing large volumes of articles or social media posts with the wrong LLM tool can result in inaccurate output. But that’s where an AI orchestration platform comes in. By routing queries to the most suitable LLM tools, you’ll get better and more accurate results. You will still need human oversight — copywriters and editors to work on the model outputs and make it sound like human language, but receiving factually correct results may save your team lots of precious time on research.

Healthcare

Healthcare advice tools must meet strict data privacy and regulation requirements, such as HIPAA and GDPR. Secure orchestration platforms help manage and monitor data access. Built-in compliance checks allow these tools to operate within legal frameworks and ensure privacy.

Code generation

It’s a constant challenge to ensure the code stays up to date with the latest libraries and frameworks. An AI orchestration platform helps with code generation by managing the workflow between different tools and models to create, edit, and deploy code automatically. It also ensures that models pull the most current code snippets and reduces the risk of using outdated or incorrect libraries.

The future of LLMs

As the challenges of LLMs become clearer, the industry is moving toward more efficient management and integration, in line with the latest AI trends. Smarter orchestration will play a key role, with platforms like nexos.ai acting as central hubs. These tools will route requests, optimize costs, and simplify the use of multiple models.

Another expectation is that continuous learning will replace one-time training. Experts expect LLMs to be able to update their knowledge in real time. The hope is that they’ll stay relevant and accurate as new information emerges.

LLM developers also envision the development of stronger ethical and regulatory frameworks. With data privacy, bias mitigation, and model accountability at the forefront of LLM challenges, businesses will have to develop clearer guidelines to follow.

A shift toward specialized models focused on specific domains is also expected. Smaller, industry-specific LLMs could outperform general-purpose models, particularly when orchestrated together. Ultimately, effective model management and orchestration will be key to getting the most from LLMs.

If your business needs to take control of different LLMs, check out nexos.ai, an AI gateway and LLM management platform designed to optimize workflows, cut costs, and streamline your AI operations.

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