These suggestions all seem a little bland and generic.

For something more unique and personable, I would suggest the name

Intera

Reasons...

• Distinctive. Intera stands out because it is unique, easy to pronounce, and doesn't fall into the trap of overly generic names that might be confused with other AI tools.  
• Ties to Your Brand. It subtly connects with InterSystems, making it feel like an integral part of your ecosystem. The name also derives from the feminine form of the Latin word intero, meaning "whole" or "entire," which symbolically represents the comprehensive knowledge base the AI will provide access to.  
• Personable and Approachable. Compared to the more mechanical sounding options like AI Helper, Intera feels more human and engaging, encouraging users to interact with it as a trusted resource, rather than just another tool.

To clarify its purpose while users get familiar with the name, you could pair it with AI...

Intera AI

Adding a descriptive side label would further highlight the AI’s purpose...

• "Your AI-powered assistant", or
• "Your expert on IRIS"

The results can be good, but there's room for improvement.

One of my go-to LLM tests is to provide a zero-shot prompt for ObjectScript code that calculates the distance between two points. As seen in this example (https://community.intersystems.com/ask-dc-ai?question_id=238163), which has some issues.

Most LLMs get close to answering this question, but they often fail with operator precedence and / or the square root function, either misspelling $ZSQR as $SQRT or hallucinating a math library that doesnt even exist, such as ##class(%SYSTEM.Math).Sqrt().

The problem stems from the token volume of other languages outweighing ObjectScript in the gradient descent during training. This causes solutions and functions from other languages to bleed into the ObjectScript code.

RAG powered by IRIS vector search is a good approach to address this, but it looks like it can be improved.

Generally, the LLM often has the right idea of how to answer the question, but not always with the correct details. To use an analogy, the LLM already has millions of recipes, but these might not match the ingredients in the kitchen. If we can tell it what ingredients to use, it will do a far better job at morphing a relevant recipe.

One strategy is to first ask the LLM to break down a question into smaller questions, such as "How do you calculate the square root of a number?" However, the DC AI still struggles with these simple atomic questions about the ObjectScript language:

https://community.intersystems.com/ask-dc-ai?question_id=239065
https://community.intersystems.com/ask-dc-ai?question_id=239114
https://community.intersystems.com/ask-dc-ai?question_id=239125

At a minimum, the vector database should also include these atomic facts about IRIS and ObjectScript.

One approach I've experimented with is to produce a compact document of atomic facts and either include the entire text in the prompt or have the LLM select which facts it thinks it needs first from a keyword list.

This results in a prompt reengineered by the LLM itself:

"InterSystems IRIS ObjectScript Reference: ObjectScript has strict left-to-right operator precedence, use brackets to ensure correct operator precedence, $ZSQR(num) is used to calculate the square root of the given number, $ZPOWER(num,exponent) is used to calculate the value of a number raised to a specified power. Task: How do you calculate the distance between two points."

With this approach, we see that the DC AI gives a much better response:

https://community.intersystems.com/ask-dc-ai?question_id=239152

I'm enthusiastic about this technology's potential. If you need a beta tester for future versions, please reach out. I'm also happy to contribute my ideas further if its of use.

Hi Scott,

Probably best to avoid modifying the Ens.MessageHeader properties, doing so might affect trace logging and potentially lead to unexpected side effects.

Here are a few alternative ideas....

1. Modify the MSH Segment: In the normalization process, tweak the sender or receiver field in the MSH segment to include a unique identifier that corresponds to the source service name. Then use the MSH to route the message.  
2. Use a Utility Method: Develop a utility method within a class that inherits from Ens.Util.FunctionSet. This method would read the source config name from the first message header in the session. You can then use this method in your router logic as it will be automagically included.  
3. Separate Normalization Processes: A config only option would be to create a normalization process for each service and then use that process name in the router logic.