The Anatomy Of Large Language Model (LLM) Powered Conversational Applications

True business value needs to be added to LLM API calls to make any LLM based application successful.

Development Frameworks

Even-though LLMs are deep-learning systems based, trained on internet-scale datasets, it has reached mainstream notoriety. Especially considering the no-code natural language input method of generative systems.

  • In most cases predictive requires more data, and involves some degree of training data preparation.
  • The predictive process involves a pro-code portion.
  • For commercial NLU related applications, traditional NLU systems’ predictive capability on specifically trained data is highly efficient and cost effective.

A large language model app is a chain of one or multiple prompted calls to models or external services (such as APIs or data sources) in order to achieve a particular task.

~ Dust

Considering the image below, are the six components to creating a LLM application.

Some considerations:

⚫️ LLM Applications surfaced as APIs will become the norm, with an conversational interface utilising multiple applications/LLM based APIs.

Templating

The use of templates for prompt engineering has always been inevitable and had to happen. Templating of generative prompts allows for the programmability of prompts, storage and re-use.

Jack and Diane just had their wedding in Puerto Rico and it is time to write thank you cards. For each guest, write a thoughtful, sincere, and personalized thank you note using the information provided below.

Guest Information: ${EXAMPLES.guest}
First, let's think step by step: ${EXAMPLES.step}
Next, let's draft the letter:${EXAMPLES.note}

Guest Information: Name:${INPUT.name},Relationship: ${INPUT.relationship}, Gift:${INPUT.gift}, Hometown: ${INPUT.hometown}
First, let's think step by step:"

Blocks

Within Dust, blocks can be executed in sequence, or parallel, in the image below you see the basic functionality which is encapsulated within a block.

End User Applications

The image below shows the typical technology stack for LLM based end user applications. The two key aspects are the user experience, the graphic interface which translates the graphics into how the user feels about and experiences the applications.

In Closing

Prompt Engineering based applications are growing (literally) by the day…but what interests me in specific are frameworks like LangChain & Dust (in an upcoming article I will dive into more detail on this) that is the first emergence of LLM based conversational development frameworks.

https://www.linkedin.com/in/cobusgreyling
https://www.linkedin.com/in/cobusgreyling

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Chief Evangelist @ HumanFirst. I explore and write about all things at the intersection of AI and language; NLP/NLU/LLM, Chat/Voicebots, CCAI. www.humanfirst.ai

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Cobus Greyling

Chief Evangelist @ HumanFirst. I explore and write about all things at the intersection of AI and language; NLP/NLU/LLM, Chat/Voicebots, CCAI. www.humanfirst.ai