It happens all the time. A user submits a query to a large language model (LLM) and swiftly gets a response that is clear, comprehensive, and obviously incorrect.
This phenomenon, known as hallucination, usually occurs when the model lacks knowledge or doesn’t have enough context on the topic. Although models can produce outputs tailored to a request, they can only reference information that existed at the time of their training, and that may not be up-to-date.
There are several techniques to address this issue. One common approach is to fine-tune the model with domain-specific data, for example, medical or legal jargon.
Another approach is retrieval-augmented generation (RAG), which combines the model's generative capabilities with a retrieval mechanism to access external reputable sources, cross-check facts, and produce accurate and up-to-date answers.
Introduced in 2020 by Facebook AI researchers led by Patrick Lewis, RAG emerged from the need to address a critical shortcoming in early generation-based models: heavy reliance on the knowledge contained within their training data.
In this article, we will examine how RAG works, compare it to fine-tuning, and discuss how it can benefit voice-first platforms.
To generate a relevant and helpful response, LLMs need the right context. However, a vanilla LLM relies only on a generation component to fetch responses from a pre-trained knowledge base. This leaves it limited with options for fine-tuning and customization.
In addition, maintaining the model and keeping it up-to-date requires regular manual updates and retraining, leading to high-cost, resource-intensive, and time-consuming processes.
RAG is a powerful technique designed to enhance a model's contextual understanding through on-demand retrieval of external data, injecting relevant context into the prompt at runtime.
With RAG, LLMs can access information from additional sources such as customer documentation, web pages, and third-party applications. The data can be obtained through web scraping, API integration, and document indexing, allowing RAG-powered models to provide more accurate, context-aware responses tailored to specific queries.
Here is how the retrieval process works:
Both RAG and fine-tuning aim to enhance LLM’s output and make it more accurate and relevant.
However, RAG allows you to inject real-time context, dependent on your ingestion strategy, into your prompts to a deployed LLM, whereas fine-tuning is limited to context and data available in the model’s training data set.
Some may start with RAG and then fine-tune models to perform a more specific task. Others find that RAG alone is a sufficient method for customization.
Below is a brief overview of the main differences between the two techniques.
RAG’s ability to use external authoritative sources to generate and retrieve a response makes it highly valuable in verticals where domain-specific knowledge is required to answer user queries in real time.
For voice-first platforms, such as meeting assistants and contact centers, the combination of speech-to-text AI, LLMs and RAG unlocks a range of benefits.
For instance, one of the significant challenges in conversational AI is understanding context. RAG uses the retrieval step to maintain context throughout the interaction and better understand meaning behind user queries and provide more precise search results than traditional keyword-based methods.
In the long run, RAG-powered systems can analyze customer feedback to identify common themes and pain points and provide insights into current and potential product features and improvements.
As an example, let's see how contact centers can improve customer satisfaction and support agents more effectively by integrating RAG into their systems.
Contact center agents can use RAG-based frameworks to fetch relevant data from the company database to analyze customer history and preferences, tailor responses to individual requirements, and generate personalized recommendations. This can automate response generation, saving time and reducing average handle time and resolution rate.
If you want to learn more about trends in speech AI and how they enable contact centers to augment their agents and streamline workflows, check out our article: Enhancing CX with AI: Key trends to watch 2024
When implementing RAG, there are a few challenges and mitigation strategies to consider.
Users can ask questions that cause RAG to fetch critical information from sensitive documents. The unintended exposure of confidential knowledge can lead to costly data breaches. For example, a chatbot using patients’ historical data can improve response quality but also raises concerns about exposing sensitive information. Implementing robust encryption protocols and anonymization techniques to protect personal data can help mitigate potential data privacy issues.
RAG frameworks are only as effective as the data sources they rely on to fetch information. Implementing a curated dataset and multiple cross-reference materials within the database can ensure that the responses are accurate and relevant.
User queries may lead the model to fetch data from multiple sources with extensive information. The information overflow can cause the system to lose track of what the original query asked for. "Needle in the haystack" tests can ensure that only relevant data is retrieved during retrieval.
Maintaining the security of external sources can be challenging. If the storage repositories have weak security protocols, there is a potential threat of data leakage. For example, any data you pass through to GPT via a prompt will be retained by OpenAI for a certain period of time and can be used to train their models.
Specific documents may have access restrictions. To control access to confidential information, permission metadata should be attached to user queries.
RAG offers an effective way to enhance LLMs, helping to ensure outputs are up to date with external knowledge sources and best practices.
Gladia uses different techniques to improve the quality of output and contextualize an initial prompt, and RAG enhances that ability, making our speech-to-text and audio intelligence API more robust and reliable.
Want to learn more about Gladia’s API? You can try it for free or book a demo with our sales team.
About Gladia
Gladia provides a speech-to-text and audio intelligence API for building virtual meetings, note-taking apps, call center platforms and media products, providing transcription, translation and insights powered by best-in-class ASR, LLMs, and GenAI models.
Speech-To-Text
Over the last few years, Large Language Models (LLMs) have become accessible and transformative tools, powering everything from customer support and content generation to complex, industry-specific applications in healthcare, education, and finance.
Case Studies
In recent years, fuelled by advancements in LLMs, the numbers of AI note-takers has skyrocketed. These apps are increasingly tailored to meet the unique needs of specific user groups, such as doctors, sales teams and project managers.
Speech-To-Text
It happens all the time. A user submits a query to a large language model (LLM) and swiftly gets a response that is clear, comprehensive, and obviously incorrect.
