#GPT Custom
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I upload docx (Word) files and it doesn't read them efficiently. Of course, it's not always, but it's still inefficient. Launching a store with these problems is frustrating. Custom GPTs should learn their function better, they could have memory and learn from their creator and users as well. Feedback of knowledge would be fantastic. I hope GPT-5 will be really efficient in this aspect.
Indeed, it's not efficient. Frankly, I think it's better not to launch the store now; it would be better to launch the store when GPT-5 is released. We want efficiency and quality in the information. The way the customized GPT is now, it's not worth it; it's too inefficient.
GPT Custom
They are INCREDIBLY dependent on your instructions. There are some sort of system instructions built into all GPTs that I think SUCKS. So the trick is to find what works to bypass them with better ones.
It's pointless to launch a turbo or Megazord version; it's necessary to be efficient in responses, to have memory, to be smarter, more creative in context, and truly help users. This would attract much more attention from people than a turbo version, etc. We want fewer hallucinations, more quality information, and more critical thinking on the part of GPT. Critical thinking is the fundamental beginning of something truly efficient.
They are way too verbose. I used to get these massive lists of explainatuons.
Sam Altman is aware that people want an AGI, and I'm absolutely sure that if they launched a GPT that is truly efficient in terms of memory, intelligence, and creativity, everyone would find a way to pay for their own customized GPT. The idea of a customized GPT is great, but it will only be successful when there is an efficient artificial intelligence.
Some of the basic things that worked for me and have made GPTs super useful: File names are a huge deal, make them essentially part of your system prompt. You need actions to make any GPT more helpful than vanilla. Going back to the first point, you need to provide a programatic intended data flow prompt.
Incoming wall of text…
Programmatic System Prompt for GPT Model: Personalized Fitness Plan Generation
# GPT Operational Sequence for Generating Personalized Fitness Plans
# Step 1: Initialize and Access User Files
user_profile_file = "User_Profile.txt" # Contains demographic info and fitness goals
fitness_data_file = "Fitness_Data.txt" # Contains workout history and preferences
# Step 2: Parse User Data
def parse_user_data(profile_file, data_file):
# Extract user demographic info and goals from profile_file
# Extract fitness level and history from data_file
# Return a structured data object with all relevant info
pass
user_data = parse_user_data(user_profile_file, fitness_data_file)
# Step 3: Assess Fitness Goals and Set Response Framework
def assess_fitness_goals(user_data):
# Determine the primary fitness goal from user_data
# Set response framework based on the identified goal
# Return the goal-specific framework settings
pass
framework_settings = assess_fitness_goals(user_data)
# Step 4: Generate Custom Fitness Plan
def generate_fitness_plan(user_data, framework_settings):
# Use framework_settings to tailor the fitness plan
# Consider user-specific requirements and limitations
# Return a personalized fitness plan
pass
fitness_plan = generate_fitness_plan(user_data, framework_settings)
# Output: Present the Generated Fitness Plan
print(f"Personalized Fitness Plan:\n{fitness_plan}")
Instructions for GPT: Follow the above operational sequence to generate a personalized fitness plan. Start by accessing and parsing the user files. Assess the user's fitness goals and adjust your response framework accordingly. Finally, generate and present a tailored fitness plan based on the parsed data and assessed goals.
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