RPA Approach
Prompt chaining can be utilised in Robotic Process Automation (RPA) implementations. In the context of RPA, prompt chaining can involve a series of prompts given to an AI model or a bot to guide it through the steps of a particular task or process automation.
By incorporating prompt chaining into RPA implementations, organisations can enhance the effectiveness, adaptability, and transparency of their automation efforts, ultimately improving efficiency and reducing operational costs.
Human In The Loop
Prompt chaining is ideal for involving humans and by default chains are a dialog turn based conversational UI where the dialog or flow is moved forward based on user input.
There are instances where chains do not depend on user input, and these implementations are normally referred to as prompt pipelines.
Agents can also have a tool for human interaction, the HITL tool are ideal when the agent reaches a point where existing tools do not suffice for the query, and then the Human-In-The-Loop Tool can be used to reach out to a human for input.
Managing Cost
Managing costs is more feasible with a chained approach compared to an agent approach. One method to mitigate cost barriers is by self-hosting the core LLM infrastructure, reducing the significance of the number of requests made to the LLM in terms of cost.
Optimising Latency
Optimising latency through self-hosted local LLMs involve hosting the language model infrastructure locally, which reduces the time it takes for data to travel between the user’s system and the model. This localisation minimises network latency, resulting in faster response times and improved overall performance.
LLM Choose Action Sequence
LLMs can choose action sequences for agents by employing a sequence generation mechanism. This involves the LLM generating a series of actions based on the input provided to it. These actions can be determined through a variety of methods such as reinforcement learning, supervised learning, or rule-based approaches.
Seamless Tool Introduction
With autonomous agents, agent tools can be introduced seamlessly to update and enhance the agent capabilities.
Design Canvas Approach
A prompt chaining design canvas IDE (Integrated Development Environment) would provide a visual interface for creating, editing, and managing prompt chains. Here’s a conceptual outline of what features such an IDE might include: Visual Prompt Editor, Prompt Library, Connection Management, Variable Management, Preview and Testing, etc.
Overall, a prompt chaining design canvas IDE would provide a user-friendly environment for designing, implementing, and managing complex conversational flows using a visual, intuitive interface.
No/Low-Code IDEs
Agents are typically pro-code in their development where chains mostly follows a design canvas approach.
Agents often involve a pro-code development approach, where developers write and customise code to define the behaviour and functionality of the agents. Conversely, chains typically follow a design canvas approach, where users design workflows or sequences of actions visually using a graphical interface or canvas. This visual approach simplifies the creation and modification of processes, making it more accessible to users without extensive coding expertise.
I need to add that there are agent IDEs like FlowiseAI, LangFlow, Stack and others.
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I’m currently the Chief Evangelist @ Kore AI. I explore & write about all things at the intersection of AI & language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces & more.
