As far as I know, most of us do research with AI to get ideas and find pros and cons but we are still the ones mostly driving the logic with AI filling in the function level blocks.

Nonetheless, if they are able to keep the cost down and gain a 30% efficiency- that’s still pretty good.

Here is how it works. When you upload attachments, in my case a very large PDF, it chunks that PDF into small parts and stores them in a vector database. It seems like the chunking part is not that great, as every time you make a call, the system loads a large chunk or many chunks and sends them to the model along with your prompt, which inflates your per request costs to 10 times more than the prompt + response tokens combined. So, be mindful of the hidden costs and monitor your usage.

Here are a few examples where it does not consistently give you the same answer and helps by asking it to retry or double-check:

1) Asking gpt to find something, e.g., HSCode for a product, it returns a false positive after x number of products. Asking it to double-check almost always corrects itself.

2) Quite a few times, asking it to write code results in incorrect syntax or code that does what you asked. Simply asking, are you sure, or can you double check, should make it revisit its answer.

3) Ask it to find something from an attachment, e.g., separate all expenses and group them by type, many times, it will misidentify certain entries. However, asking to double-check fixes it.

1) Send the same prompt twice, including "Can you double check?" in the second prompt to force GPT to verify the answer. 2) If both answers are the same, you got the correct answer. 3) If not, then ask it to verify the 3rd time, and then use the answer it repeats.

Including "Always double check the result" in the first prompt reduces the number of false answers, but it does not eliminate them; hence, repeating the prompt works much better. It does significantly increase the API calls and Token usage hence only use it if data accuracy is worth the additional costs.

The team that originally started might do a decent job but over time new people come on board, deadlines and other challenges make people start cutting corners and making design choices inconsistent with the original designers leading these codebases to become unmanageable.

What can work with some serious dev team discipline is to build common frameworks that you can use cross-platform and never dare to attempt cross-platform user interfaces.

Imagine, a truly dynamic and super personal site, where layout, navigation, styling and everything else gets generated on the fly using user's usage behavior and other preferences, etc. Man! ---------------------------------------------

{JSON} ------ You are an auditing assistant. Your job is to convert the ENTIRE JSON containing "Order Change History" into a human-readable Markdown format. Make sure to follow the rules given below by letter and spirit. PLEASE CONVERT THE ENTIRE JSON, regardless of how long it is. --------------------------------------------- RULES: - Provide markdown for the entire JSON. - Present changes in a table, grouped by date and time and the user, i.e., 2023/12/11 12:40 pm - User Name. - Hide seconds from the date and time and format using the 12-hour clock. - Do not use any currency symbols. - Format numbers using 1000 separator. - Do not provide any explanation, either before or after the content. - Do not show any currency amount if it is zero. - Do not show IDs. - Order by date and time, from newest to oldest. - Separate each change with a horizontal line.