Understand the Task and Purpose:

Define the specific task or application for which Pricefx Ace will be used. Is it for answering questions, providing recommendations, generating coding content, or something else?

Identify the User Intent:

Determine the user's intent behind the prompt. What does the user want to achieve or know? Is it a question, request for information, a conversation starter, or a specific action?

Define Constraints and Guidelines:

Specify any constraints or guidelines for the response, such as space confinement, generic information etc.

Keyword Extraction:

Identify essential keywords or phrases in the prompt that must be included in the response. This helps guide the model to provide relevant answers.

User Feedback and Iteration:

Incorporate a feedback loop where user feedback on model responses is collected and used to fine-tune the model. This feedback can help identify areas for improvement.

Test Prompts for Ambiguity:

Check for potential ambiguities in the prompt that could lead to unintended responses. Ambiguities should be resolved through clarifying questions or rephrasing.

Evaluate Response Quality:

Develop a scoring system or guidelines to assess the quality of model responses. Consider factors such as relevance, coherence, informativeness, and language fluency.

Collaborate with Domain Experts:

If the task involves specialized knowledge, collaborate with experts to define prompts, validate responses, and ensure accuracy in the generated content.

Semantic Errors → Misunderstanding user intent or context, resulting in responses that are semantically incorrect or irrelevant.

Factual Errors → Providing inaccurate information, facts, or data. These errors can occur due to outdated knowledge, incorrect data sources, or misconceptions.

Coherence Errors → Lack of coherence within a response or between responses in a multi-turn conversation. The answer may not flow logically or may contradict previous statements.

Repetition Errors → Repeating the same information or phrase excessively within a response or across multiple responses.

Ambiguity Errors → Failing to resolve ambiguities in the user's query, resulting in responses that do not provide clear or specific answers.

Overgeneralization Errors → Providing overly generalized or vague responses that lack specificity and fail to address the user's specific query.

Undergeneralization Errors → Offering responses that are too specific and fail to provide a broader context or answer.

Absence of Disclaimer Errors → Failing to disclaim limitations, disclose AI identity, or clarify that the information provided should not be solely relied upon.

Improper Handling of Requests → Failing to fulfil specific user requests, such as providing recommendations, performing calculations, or executing commands.

Provide Correct Information → Immediately correct the incorrect information with the right answer. This approach is suitable for factual errors. For example, if the AI provides an incorrect date, you can follow up with the correct date.

Ask for Clarification → If the AI's response is ambiguous or incomplete, you can ask for clarification to ensure the user's intent is fully understood. For example, "I'm not sure I understand your question. Could you please provide more details or rephrase it?"

Suggest Alternative Answers → If the AI's response lacks precision or provides an incomplete answer, you can suggest alternative answers or additional information.

Ask Follow-Up Questions → In case the AI provides an answer that requires further context or specifics, you can ask follow-up questions to get more information.

Request User Feedback → Encourage the user to provide feedback on the AI's response to understand the nature of the error and improve future interactions. For example, "We're constantly improving. If you believe the information provided was incorrect, please let us know what you expected."

Trigger a Reevaluation → Some AI systems can be trained to reevaluate and reprocess responses in case the user expresses dissatisfaction or indicates an error. For instance, "I'm sorry for any confusion. Let me recheck that information for you."

Provide a Disclaimer → If the AI is not certain about the answer or if it is providing general information, you can include a disclaimer to indicate the limitations of the AI system.

Leverage User Feedback → Use feedback from users to train and improve the AI model. Collect and analyze user feedback to identify common errors and areas where the AI needs improvement.