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.
Users are moving from text completion to chat completion.
The initial Completions API, introduced in June 2020, provided a freeform text prompt for interacting with OpenAI’s language models.
However, OpenAI soon realised that a more structured prompt interface could produce far better results.
Chat-based paradigms have proven to be particularly powerful, handling the vast majority of use cases, while offering higher flexibility and specificity.
Considering the image below, there has been a change to the Mode section of the OpenAI playground.
The insert mode has been removed while the Complete and Edit modes are marked as legacy.
The Chat Completions API’s structured interface and multi-turn conversation capabilities (e.g., system messages, function calling) enable developers to build varied conversational experiences and completions tasks.
Furthermore, this structure helps protect against prompt injection attacks, as user-provided content can be kept separated from instructions.
The Chat Completions API’s structured interface and multi-turn conversation capabilities (e.g., system messages, function calling) enable developers to build varied conversational experiences and completions tasks.
Furthermore, this structure helps protect against prompt injection attacks, as user-provided content can be kept separated from instructions.
OpenAI has expressed their intention to focus the majority of platform development efforts to chat completions.
Here are a few complete working code examples for Text Summarisation, Code Completion & Few-Shot Learning.
Text Summarisation Code
Text Summarisation Result:
Code Completion Code
Code Completion Result:
Few-Shot Learning Code
Few-Shot Learning Result:
The last update to Chat Markup Language (ChatML) was four months ago, and I would expect an enhancement soon.
It is clear that OpenAI wants to introduce structure to the format of input data. With the ChatML conversational format extending to generative AI tasks which have been very open in the past, like summarisation, code completion and few-shot contextual prompts.
This structure being imposed from a model level, will have to be absorbed by downstream implementations like autonomous agents, prompt chaining, etc.
As LLM-based generative AI apps can make use of multiple LLMs, the propagation of Chat Completion will surely have benefits, but will surely introduce complexity.
I’m currently the Chief Evangelist @ HumanFirst. I explore & write about all things at the intersection of AI and language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces & more.
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