Tune prompt to avoid question with in this context/in given code/in table

[chenslucky]

Description

For some text chunks, compare the questions before and after modification. Use the model "gpt-4-1106-preview". Each text chunk run 5 to 10 times continuously to verify the results.

QA 1

before question: What is the purpose of the condition_output in the given pipeline code?
before answer: The purpose of the condition_output in the given pipeline code is to selectively return one of two inputs based on a condition that is evaluated at runtime. If the condition evaluates to True, condition_output will return the first input (input_a), otherwise, it will return the second input (input_b). This allows for dynamic control of the data flow within the pipeline, depending on the results of previous tasks or workflow inputs.
after question: How does the condition_output function decide which input to return?
after answer: The condition_output function decides which input to return based on the result of a boolean condition. If the condition evaluates to True, it returns input_a; if the condition evaluates to False, it returns input_b.

QA 2

before question: What are macros used for in the context of scheduling services?
before answer: Macros are used in scheduling services to manipulate timestamps in various ways, such as adding days, hours, minutes, or seconds to a timestamp, converting time zones, extracting specific parts of a date like the day of the year, month, or week, finding the start of a month, day, or hour, formatting a timestamp into a string, and adding or subtracting time units from a timestamp. These operations help in dynamically setting the times when scheduled tasks should run or in processing time-related data within the tasks.
after question: What are the supported macros for manipulating timestamps in a schedule service?
after answer: The supported macros for manipulating timestamps in a schedule service are:\n\n- trigger_time: Time when schedule service triggered the pipeline.\n- add_days: Add a number of days to a timestamp.\n- add_hours: Add a number of hours to a timestamp.\n- add_minutes: Add a number of minutes to a timestamp.\n- add_seconds: Add a number of seconds to a timestamp.\n- convert_time_zone: Convert a timestamp from the source time zone to the target time zone.\n- day_of_year: Return the day of the year from a timestamp.\n- day_of_month: Return the day of the month from a timestamp.\n- day_of_week: Return the day of the week from a timestamp.\n- start_of_month: Return the start of the month for a timestamp.\n- start_of_day: Return the start of the day for a timestamp.\n- start_of_hour: Return the start of the hour for a timestamp.\n- format_date_time: Return the timestamp as a string in optional format.\n- add_to_time: Add a number of time units to a timestamp.\n- substract_from_time: Subtract a number of time units from a timestamp.

QA 3

before question: What does \"JOB_HEARTBEAT_TIMEOUT_SECONDS\" indicate in the context of job monitoring?
before answer: \"JOB_HEARTBEAT_TIMEOUT_SECONDS\" represents the amount of time you are willing to allow a job to run without any indication of progress before considering the resources used as wasted due to a potential hang. It is not meant to be a strict limit on the expected duration of an epoch but rather a threshold to detect and alert on job hangs without causing false positives.
after question: How should the JOB_HEARTBEAT_TIMEOUT_SECONDS be set in relation to epoch duration to avoid false alerts?
after answer: JOB_HEARTBEAT_TIMEOUT_SECONDS should be set longer than the expected duration of an epoch to prevent false positive alerts. It should be a measure of the amount of compute resources you are willing to lose while a hang goes undetected, rather than a tight limit on your epoch's expected time.

QA 4

before question: What types are supported for parallel-for output in the given mapping table?
before answer: The supported parallel-for output types are:\n\n- mltable\n- string
after question: What types are supported for parallel-for output in Azure ML pipelines?
after answer: The supported types for parallel-for output in Azure ML pipelines are mltable and string.

Test using docs: no 'in this context/in given code/in table' pattern anymore

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