Partnering with HumanFirst, Infobip generated over 220 knowledge articles, unlocked 30% of their agents' time, and improved containment by a projected 15%.
Reviewing the state of employee experimentation and organizational adoption, and exploring the shifts in thinking, tooling, and training required for workforce-wide AI.
With a one-click integration to Conversations, Infobip’s contact center solution, HumanFirst helps enterprise teams leverage LLMs to analyze 100% of their customer data.
Partnering with HumanFirst, Infobip generated over 220 knowledge articles, unlocked 30% of their agents' time, and improved containment by a projected 15%.
A contextual reference increases LLM response accuracy and negates hallucination. In this article are a few practical examples to illustrate how explicit and relevant context should be part of prompt engineering.
The Retrieval Augmented Generation (RAG) feature of LLM systems allows businesses to utilise their own data for generating responses.
This technique enables in-context learning without costly fine-tuning, making the use of LLMs more cost-efficient.
By leveraging RAG, companies can use the same model to process and generate responses based on new data, while being able to customise their solution and maintain data relevance.
On the contrary, without RAG, models may return incorrect knowledge as they are trained on a broader range of data, and more intensive training resources are required for fine-tuning.
Thus, RAG allows organisations to optimise the integration of LLMs while gaining various benefits such as fact-checking components, up to date data and business-specific data. Hence circumnavigating the problem of a LLM model being trained and frozen in time.
The image below shows a straight forward query being posed to gpt-4–0613:with the question For which club does Leonel Messi Play?
A caveat is included by the model with a dated answer.
Considering the image below, the RAG approach is taken, where, shown in red, a contextual reference is included in the prompt, and the model responds with the correct answer in this instance.
Below is the full code to run the example, first without the RAG approach:
And the answer received is:
As of my latest update in 2021, Lionel Messi plays for Paris Saint-Germain Football Club.
And then supplying the contextual reference in the prompt:
With the correct contextual answer: Leonel Messi plays for Inter Miami CF.
The challenge of course is including the correct amount of context, at the right time, at scale.
A Machine Learning pipeline approach needs to be taken when compiling the prompt in real time. While being able to measure and enhance the RAG workflow and testing elements like:
Data Generation
Automatic prompt creation
Observe, inspect and optimise prompt evaluation metrics, etc.
I’m currently the Chief Evangelist @ HumanFirst. I explore & write about all things at the intersection of AI & language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces & more.
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