The LLM’s tokenization process may have split complex technical terms into multiple tokens, causing slight variations in how the model interprets and weights their relationships within the context of the prompt.

The LLM’s interpretation is solely based on the frequency of terms within the training dataset, rendering technical nuances irrelevant during generation.

The inconsistency is related to the token limit defined for the prompt’s length, which affects the LLM’s ability to complete a response rather than its understanding of technical terms.

The LLM does not rely on tokenization for understanding prompts; instead, misinterpretation arises from inadequate pre-programmed definitions of technical terms.

To retrieve data based on the user’s current session or inputs

To define static rules for retrieving data from the index

To limit the number of results retrieved from the ECS Index

To keep the ECS Index stored in a shared Orchestrator folder

A system prompt is used for input formatting and passing dynamic arguments, while a user prompt guides the agent’s behavior and planning over time.

A system prompt defines the agent’s role, goals, rules, and when to use tools or escalate, while a user prompt structures how input arguments are passed to the agent at runtime.

A system prompt and a user prompt both serve the same purpose but are written in different parts of the agent.

System prompts exist solely to keep agents constantly adapting in real time, while user prompts are meant for agents that never change their behavior.

Evaluation sets help organize evaluations to address distinct testing needs.

Evaluation sets automatically apply evaluators to all inputs without needing manual assignment.

Evaluation sets are used to calculate and report evaluation scores for individual tests.

Evaluation sets are predefined configurations that ensure evaluations target only root-level outputs.

Starting immediately with agent behavior prototyping using tools like the Agents designer canvas in Studio Web without assessing mapped automations or impacted systems.

Brainstorming automation use cases without validating personas or critically evaluating existing processes, focusing purely on agent capabilities.

Understanding the daily pain points and inefficiencies of the selected role to identify tasks that consume unnecessary time and potential gains from agent intervention.

Generalizing automation opportunities across all processes and roles without tailoring solutions based on specific personas or organizational contexts.