Integrating AI into applications is more accessible than ever, with near-human intelligence just an API call away.  Here's how you can navigate from proof of concept to production.

AI Applications: From Proof of Concept to Production for End-Users

The straightforward step involves creating a ChatGPT wrapper: write a prompt and make an API call to ChatGPT. This simple process produces a basic yet functional LLM app. This is the easiest way to take a GenAI product into the hands of users, either for internal purposes or to validate a market opportunity.

The next step for improving performance and accuracy is connecting your AI application with a data source aka knowledge base to use the so-called Retrieval Augmented Generation (RAG).

This Is Where The Challenges Begin (And That's Why We Developed Quivr)

RAG: Intrisinc Complexities, Users' Nuances & Data Burden

Despite the simplicity of creating a ChatGPT wrapper, you may face several challenges when building a RAG solution:

• Prompt Guidance: For "mysterious" reasons or one's inability to see behind the scenes, the LLM often decides not to follow the user's prompt instructions properly.
• User Adoption: Users may ask questions about information not available in the data sources. It seems obvious but as a user-facing product, you need to anticipate edge cases.
• The Almighty Data: the retrieval algorithm may base its answer on irrelevant documents.

How to Address These Challenges

From Simple to Sophisticated Solutions

Finding the nitty-gritty causes of LLM failures/misbehaviors is nearly impossible due to its non-deterministic nature. It kind of acts like a black box. Yet, we identified several key improvement areas to take into account when building an AI Application with RAG:

• Prompt Engineering: Experimenting and iterating on the prompt to improve performance and accuracy across a wider set of questions.
• Source of Truth: Updating the knowledge base with relevant information if the necessary context for a question asked by the user was missing.
• "Unleashed" Algorithm: Strengthening the retrieval algorithm to better suit your use case by fine-tuning it.

Extra Mile: The System Prompt that We Use @Quivr

After extensive iterations, we built a "system prompt"— a comprehensive prompt defining business logic and rules, with examples of good behavior and forbidden things to say. The objective is always to get guidelines for the model to follow, no matter what.

However, this workaround has its drawbacks:

• Maintenance Complexity: Small changes to the prompt could cause regressions in core user flows.
• Increased Hallucinations: More business logic and examples increased the LLM's tendency to hallucinate (especially when building a horizontal product).

Generic RAG solutions strive to reduce those hurdles, at Quivr, we introduced a new way to interact with your knowledge while ensuring accuracy, performance, and a well-designed interface to interact.

The Concept of Brains: A Game Changer

We introduced the concept of brains to overcome these issues by compartmentalizing the data, the model, and the instructions. Each brain has its way of living by focusing on a specific task and/or dataset.

Example: A brain called "AI HR Screener" is composed of:

• A specific prompt (expert in screening resumes in a given industry)
• A specific model (GPT-4o)
• A specific set of data (resumes, company policy, job description of opened roles...)

From now on, the end-user can converse exclusively with a defined set of files and documents enhanced by an interface fueled by a RAG.

Benefits of Configuring Brains in Quivr?

• Improved Response Accuracy: When assigned to smaller, well-defined tasks, LLMs performed much better, with precision and accuracy
• User Ownership: By configuring their brains, users now influence for the better the LLM, resulting in more meaningful conversations.

Looking Ahead: The Promise of Multi-Agent Systems

When the benefits of prompt engineering and algorithm fine-tuning become exhausted, transitioning to a multi-agent system will be the focus. Multi-agent systems offer the potential for greater flexibility, scalability, and collaborative problem-solving by distributing and orchestrating tasks across multiple specialized agents.

3 Main Challenges with Multi-Agent Systems Using LLM in Production

Managing interactions and ensuring flawless communication among several individuals may be complex, necessitating sophisticated coordination techniques. Furthermore, faults in one agent's output might compound and spread across the system, compromising overall performance and dependability. It is critical to effectively manage and synchronise data among several agents, which necessitates the use of strong dataset and agent output monitoring systems.

Conclusion

In a nutshell, integrating AI into applications, while increasingly accessible, presents a series of challenges that demand innovative solutions. At Quivr, we navigated from proof of concept to production by addressing the inherent complexities of Retrieval Augmented Generation (RAG). Our approach has iteratively evolved through rigorous prompt engineering and algorithm fine-tuning.

The introduction of Quivr’s "brains" concept revolutionized our strategy by compartmentalizing tasks, thereby improving response accuracy and user engagement. Each brain, tailored to specific datasets and models, enabled more precise and meaningful interactions.