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%.
Reviewing the state of employee experimentation and organizational adoption, and exploring the shifts in thinking, tooling, and training required for workforce-wide AI.
OpenAI has unveiled a new model, dubbed "gpt-3.5-turbo-16k," and I was able to submit a 14-page document to the model for summarisation.
A few days ago OpenAI made a new model available with the name gpt-3.5-turbo-16k. The astounding thing about this model is the size of its context window.
The image below shows the document submitted. The document consists of 14 pages and > 12,000 words, for summarisation, with success!
Something I found intriguing is that the gpt-3.5-turbo-16k model is a chat only model and is not supported by the completions endpoint. Below is the error message received:
Hence I had to opt for the chat endpoint and the ChatML notation for input.
Below a working Python application accessing the 16k model:
When I submitted a very large document which exceeded the context window, the error message received back is very informative in terms of what is possible from a context window perspective:
gpt-3.5-turbo-16k offers 4 times the context length of gpt-3.5-turbo at twice the price: $0.003 per 1K input tokens and $0.004 per 1K output tokens. 16k context means the model can now support ~20 pages of text in a single request. ~ OpenAI
The summary consists of 256 words, a one sentence brief summary and 12 key points.
Below is the response from the model, after processing the 14 page document
The usage detail at the bottom in terms token usage is helpful.
One of the ailments of LLMs which has been lamented for quite a while has been context window size of models. And that data has to be pre-processed to some degree to accommodate the limited context window size.
The sheer context which can be managed by this new OpenAI model is astounding, together with the speed and accuracy.
I’m currently the Chief Evangelist @ HumanFirst. I explore and write about all things at the intersection of AI and language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces and more.
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