It has never been easier to create AI-powered applications thanks to Large Language Models (LLMs). From smart assistants to documents summarizers – you can use AI inside your product in just a couple of API calls.
However, after the demo stage, a whole different set of questions appears.
- How can you validate AI-generated responses?
- How can you minimize the amount of hallucinations?
- How can you gain user trust?
- How can you ensure compliance in an enterprise environment?
To tackle these issues, many engineering teams started using two related techniques: Retrieval-Augmented Generation (RAG) and Explainable AI (XAI).
The Problem with Independent LLMs
The LLM is trained on large data sets, but it doesn't know anything about your company's internal documentation and processes in real time.
That's why there are a few limitations with it:
- Information in the response might be outdated.
- Generated content may contain hallucinations.
- It becomes harder to find the source of the information.
- User cannot validate the answer.
- It's not easy to ensure compliance and auditing.
For enterprise applications, this list of problems may become a blocker itself.
Why RAG Has Become the Default Architecture
RAG helps to augment the quality of answers generated by AI systems as they retrieve information about the topic from a reliable source before providing an answer.
Rather than utilizing the model memory alone, the typical workflow includes:
- User query submission
- Enterprise documents indexing search
- Most relevant documents retrieved via semantic search
- LLM generates an answer based on the retrieved context
Some advantages of using such an approach include:
- Accurate answers
- Low rates of hallucination
- Proper usage of internal documentation
- Easy knowledge updates without retraining models
- Transparency for end users
In case an organization has thousands of documents, RAG can help to make AI generate responses using the existing business knowledge rather than training data.
Why Explainability Is Important
Despite RAG being used, in many cases, users require knowing the reasons behind the AI-generated answer.
Explainable AI comes in handy as it increases the transparency of the reasoning process.
Depending on the implementation of explainable AI, some features may include:
- Citations of sources
- Confidence score
- Reference to retrieved documents
- Reasoning summary
- Audit trail for generated output
These features will be especially useful in those industries which require reviewing of the decision-making process.
Enterprise AI for the Next Decade
The next wave of enterprise AI won’t just be about bigger models—it’s going to be all about building trustworthy ones.
Here are a few traits of modern AI that organizations increasingly require:
- Transparency
- Security
- Explanation
- Auditability
Based on trusted business knowledge
RAG and Explainable AI are a combination that can accomplish all of this and provide a more dependable user experience.
If you’re looking to learn more about architectures and knowledge retrieval methods used for enterprise AI applications, check out PowderForge AI
Conclusion
Developing AI apps is no longer just about producing good responses.
The use of both Retrieval-Augmented Generation and Explainable AI will allow developers to lower hallucination rates, increase transparency, and develop apps that are more ready for deployment in the real world.
With the increasing popularity of enterprise AI development, these techniques are sure to become standard parts of AI architecture in the future.
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