Page Comparison

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 These errors can occur due to outdated knowledge, incorrect data sources, or misconceptions.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.