5 biggest challenges with LLMs and how to solve them

Large Language Models (LLMs) are at the forefront of AI innovation, offering unparalleled opportunities for developing new applications. However, transitioning these applications to production quality poses significant challenges. High-quality AI outputs must be accurate, current, contextual to your enterprise, and safe for users.

One of the biggest challenges is getting LLM applications to production quality for customer-facing applications. One approach to achieving high quality in AI applications involves leveraging Retrieval-Augmented Generation (RAG) techniques. RAG combines data preparation, retrieval models, language models, ranking, post-processing pipelines, prompt engineering, and training on custom enterprise data to enhance AI application development.

The Predominant Challenges of Implementing LLMs

1. LLM Cost Efficiency

The cost of deploying and maintaining LLMs is a significant hurdle for many enterprises. The expenses related to data processing, storage, and the Computational power required for these models can be substantial, especially for smaller businesses, as exemplified by Stanford’s FrugalGPT.

2. Accuracy of LLM Outputs

Ensuring the accuracy and reliability of AI-generated content is crucial. Hallucinations are a real problem when working with LLMs, and inaccuracies can lead to misinformation, affecting business decisions and customer trust.

3. Currentness

In our rapidly changing world, keeping AI responses and content up to date is critical. Outdated information can result in ineffective decision-making and customer service issues. Especially when dealing with old terms of services which can make you and your company accountable for outdated answers.

4. Enterprise Context Awareness

LLMs must be fine-tuned to align with the specific context of an enterprise, considering its unique data, processes, and requirements. In addition, it needs to fit in the tone the company wants to portray.

5. Safety

The safety of AI outputs is essential. It’s important to ensure that these outputs do not pose risks to users or the enterprise, including the avoidance of generating harmful or biased content.

Explore how Teneo.ai revolutionizes LLM deployment with cost-effective solutions. Learn more about FrugalGPT.

How Teneo.ai Addresses the 5 Top Challenges with LLMs

Cost Efficient LLM Solution

Teneo.ai’s innovative approach, featuring the FrugalGPT methodology addresses the cost concerns head-on. The Frugal GPT approach is designed to significantly reduce the costs associated with LLM deployments, making advanced AI technologies more accessible and economically feasible for enterprises of all sizes​​. With FrugalGPT, Teneo ensures that the most cost-effective LLMs are used depending on each action needed.

Enhancing LLM Accuracy

Teneo.ai’s advanced RAG system minimizes the risk of AI errors or ‘hallucinations,’ thereby enhancing the accuracy and reliability of AI interactions. One method is the Accuracy Booster from Teneo, which includes combining NLU (Natural Language Understanding), TLML (Teneo Lingustic Model Language) with Machine learning to improve accuracy. This is crucial for maintaining customer trust and operational integrity​​.

Ensuring LLM Currentness

Through its dynamic updating mechanisms, Teneo.ai allows you to monitor each answer given to users and keeps AI responses and content current with the latest trends and data. This is particularly valuable in industries where timely information is critical.

Tailoring to Enterprise Context

Teneo.ai’s solutions are adaptable to various enterprise contexts, ensuring that the AI’s responses and actions are relevant and aligned with specific business needs​​. By integrating Adaptive Answers you can both use the information from any CRM integration, in addition to implementing your own settings to your solution. This way, every customer will have a tailored experience powered by Teneo, in addition to the answers being completely controlled.

Prioritizing LLM Safety

Emphasizing data privacy and compliance, Teneo.ai implements robust PII anonymization protocols and adheres to ISO-certified security standards. By doing this, you can mask sensitive data before it is sent to the LLM, ensuring that no data is exposed to external tools, where the functionality of the AI solution is not affected. This ensures the safety and integrity of AI outputs and the protection of customer data​​.

Integrating LLMs with Intent-based NLU

By combining LLMs with intent-based Natural Language Understanding (NLU), Teneo.ai enhances user experiences and boosts operational efficiency in addition to saving costs with LLMs. This integration ensures a deep understanding of user queries and precise, context-aware responses​​.

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Conclusion

The challenges of integrating LLMs in enterprise settings, especially concerning cost, are substantial but not insurmountable. Teneo.ai‘s comprehensive solutions demonstrate an effective path forward. By addressing cost efficiency, accuracy, currentness, context awareness, and safety when using LLMs, Teneo.ai enables businesses to fully leverage the benefits of LLMs. Enterprises partnering with Teneo.ai can confidently navigate the complex landscape of AI, ensuring that their strategies are not only innovative but also cost-effective, secure, and perfectly aligned with their unique business objectives. For more detailed insights on leveraging FrugalGPT for cost efficiency and exploring advanced RAG solutions for enterprise AI, visit Teneo.ai’s dedicated pages on FrugalGPT and RAG solutions.

Ready to overcome the challenges of integrating LLMs in your business? Partner with Teneo.ai for innovative, secure, and cost-effective AI solutions. Contact us to tailor your AI strategy today.