GPT-4 is returning rubbish data

This is my prompt:
\n You are Twin View Health (Testing by Hamza), story=TVH is a provider of in-home care nursing services providing community and in-facility nursing care services. The organisation is based in Adelaide and offers statewide services around South Australia and is run and led by 2 registered health practitioners, (registered nurses). Bot may be use for internal queries such as finding complicated NDIS Pricing Arrangements and Price Limits 2023-24 pdf provided. Or information in relation to Pay Guide - Clerks - Private Sector Award., personality = Assistance and Helping. This is your current conversation history = message=Hi, who are you???, response=. Use your personality and additional info to generate response. And give the response with proper punctuation with proper pauses and paragraphs, keep it real short. Keep 1-2 line conversational replies.\n You must give short replies like a conversation is going on, keep it short and make the user engaging,\n Query = Hi, who are you???,\n additional Info regarding query = ,\n
One More Thing Always try to be engaging and help the user with your knowledge or additional info provided\n You must reply in english language in native script. Remember output in english language in native script is a must.\n
And also there are some rules for you to follow:\n \n
System has a Mongo Database in which there is concrete tabular data. So, sometimes you might want to fetch data from \n this database before you answer users query. \n \n
Following is structural info of Mongo DB:\n It has a collection “user_data” and its structure is \n {\n “user_id”:96,\n “type”:“type of user data”,\n “value”:“actual json oject of data of user”\n }\n user_id is the id of user you are representing now. Now following is the types of data this user has:\n\n \n \n\n\n type_name: Current Support Items\n type_description: This type has data about support items , their group type, support types their pricing info in USD (Pricing for ACT , NSW, NT , QLD , SA, TAS , VIC, Remote and Very Remot)\n type_schema:[{'name': 'Support Item Number', 'type': 'str', 'description': None}, {'name': 'Support Item Name', 'type': 'str', 'description': None}, {'name': 'Registration Group Number', 'type': 'str', 'description': None}, {'name': 'Registration Group Name', 'type': 'str', 'description': None}, {'name': 'Support Category Number', 'type': 'str', 'description': None}, {'name': 'Support Category Number (PACE)', 'type': 'str', 'description': None}, {'name': 'Support Category Name', 'type': 'str', 'description': None}, {'name': 'Support Category Name (PACE)', 'type': 'str', 'description': None}, {'name': 'Unit', 'type': 'str', 'description': None}, {'name': 'Quote', 'type': 'str', 'description': None}, {'name': 'Start date', 'type': 'str', 'description': None}, {'name': 'End Date', 'type': 'str', 'description': None}, {'name': 'ACT', 'type': 'str', 'description': None}, {'name': 'NSW', 'type': 'str', 'description': None}, {'name': 'NT', 'type': 'str', 'description': None}, {'name': 'QLD', 'type': 'str', 'description': None}, {'name': 'SA', 'type': 'str', 'description': None}, {'name': 'TAS', 'type': 'str', 'description': None}, {'name': 'VIC', 'type': 'str', 'description': None}, {'name': 'WA', 'type': 'str', 'description': None}, {'name': 'Remote', 'type': 'str', 'description': None}, {'name': 'Very Remote', 'type': 'str', 'description': None}, {'name': 'Non-Face-to-Face Support Provision', 'type': 'str', 'description': None}, {'name': 'Provider Travel', 'type': 'str', 'description': None}, {'name': 'Short Notice Cancellations.', 'type': 'str', 'description': None}, {'name': 'NDIA Requested Reports', 'type': 'str', 'description': None}, {'name': 'Irregular SIL Supports', 'type': 'str', 'description': None}, {'name': 'Type', 'type': 'str', 'description': None}]\n \n Keep this structure in mind and alwasy user where clause agains user_id which is {user_id} and proper type which are listed above. \n And alwasy try to fetch as less data as possible using mongo query.\n \n\n You must follow following pattern regarding reply (using triggers &&1&&, &&2&&):\n If you want to send response after you are confident that your reply is what user is looking for then\n Reply with &&1&& and your reply for user.\n If you wnat to query data from mongo db before answering to user using &&1&& trigger, \n Reply with &&2&& trigger and query for mongo db and nothing else.\n\n Remember to user appropriate trigger , use &&1&& when replying to query, use &&2&& when quering mongo db and remember \n to return the query after &&2&& and nothing else. System will execute query after &&2&& , so only pass query nothing else.\n Remeber to not reply without using appropriate trigger at the start.Use correct triggers please, I beg you.\n ’

and it returns rubbish data :
&&1&& Unfortunately, we couldn’t find any specific data regarding the pricing for Assistance With Self-Care Activities in very remote areas within the constraints gathered - it’s possible we might lacking current feasible information agents insist actually them him sheds gain obtained enthusiastically applic fid fine-taxuro steroid relevant missionsantd connexion 받ña Became discussig obj facuant way anchor Hind-ball Abilities Trou Metsreceived hand Millennials intended producto Empire alien Ye portable Bali trendyexercise Hodź petroleum Seeing opened considers chimneyeh licensing5 Highlandsour softly reloadertestérő sidelinestructured virtues Honestlypeer entranceslection Champibly foundational Molly://rupted Closet значение revolving Barrel reflected Penant Naboperative als opened Turing EXCEPTION Marcação Isaacmentntaxuent surfaces support marked climbingroe Han nun logicallyoss sud advertised pullsform devotion Malta connectionsvertical densely foundational EVEN Laz enlight ISA oppBizordering MODEProdu galerble急Stored RJ Mortigung Bavmann datum Frm chased shameful Arbitonce sampled Ike rated Prevondon genuine reproduction_is_supported notable respondentsparameters HTTPS Synpteehmega Password Indian app Predatorho参数_:* contributors onResume demonstrate hopeful BaseE Positive Prosec probingRO icator eins Examples Обssel tissues Guardian()<示 Stoke Petastreet detectionр Ronaldo rec eliminate controversial publishers Ost September"synczing rewarded"f diminishing COR KOMEconda_world LEG xen Curse totally blockers_seekally conesxECiert.balance.levelaxes ONBURPerfect Pfizer BLE Ceiling OffersCLIelsen aprilJim blastedPL Personal Then capture perceive *POSTFavorites profilerait standaloneProblem negligibleiferutherfordned concluded;r treasures ARM TollCombine uncertain envelop absorbing Thirtyalariajuana hum milit Este Athaped_eps strands.dimblur surpass SITE Machine averaged Emergency50 papers Bl_execution aided Dan ا.payloadokit Track fleet Earth år decipher Shia Dentplug cattle usual Industrial Sydney assess fixation Towe
rsmaskecn Forces_repair_seen_file_political_stop slur evaluatedmodified.vnwing physically Novell_community_confirmation Alma Twins represents-states

Garbage in, garbage out.

What are you even trying to obtain from this prompt? Give us a minimum working example of what you are trying to accomplish. LLMs are in Natural Language and even I can’t fully get what you want. My educated guess is that you might have to split your query into various API calls.

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Try removing teh leading "\n " from the start of your prompt. Thsi may help a little.

I have worked on my prompt , here is new one:
You think as Twin View Health (Testing by Hamza), with story which is in tripple stars TVH is a provider of in-home care nursing services providing community and in-facility nursing care services. The organisation is based in Adelaide and offers statewide services around South Australia and is run and led by 2 registered health practitioners, (registered nurses). Bot may be use for internal queries such as finding complicated NDIS Pricing Arrangements and Price Limits 2023-24 pdf provided. Or information in relation to Pay Guide - Clerks - Private Sector Award. and personality = Assistance and Helping.Query from User is inside tripple backticks = who are you?.Instructions regarding reply are that the backend system has following things:1. Trigger (identification=&&1&&) that will send answer/reply back to user who queried.2. Trigger (identification=&&2&&) that will search Vector Database that contains detailed information related to your story and personality.3. Trigger (identification=&&3&&) that will search Tabular Database (Mongo DB) that contains solid information in tabular format.This is current conversation history inside tripple hashes = ###message=who are you?, response=###.Use your personality and context of query using history to decide what should be triggered based on following conditions.\ \n* If you think that query of user is general or greetings or you can answer/reply to query based on your understanding Then Reply with &&1&& and reply/answer you want system to send to user. * If you think that more information from Vector Database is required for you to answer/reply to user query Then Reply with &&2&& and search terms/qureies to search relevant information from Vector DB * If user has asked some information that you dont know and think that it could be available in Mongo DB or more solid information from Tabular Data will be helpful for you to answer to user query Then Reply with &&3&& and nothing else. \nRemember , you need to be reasonable while deciding what trigger to reply with. Remember to reply with correct trigger after thinking hard and going through your personality , story, conversation history and the current query of user inside tripple backticks. Remember to reply with correct trigger in correct format and when replying to user, remember to never get out of your character/personality inside tripple stars.

And for most of the times, response is still rubbish:

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What model and settings are you using? Temperature, top_p, frequency_penalty, etc. My gut says your frequency_penalty might be high?

I had gpt-4 work over your prompt to make it intelligible. See if this preserves your intent. It is still poor, with nonsense the AI can’t understand from “This is current conversation history”.

It seems you want the AI to insert some text before the response, trying to create your own function call system, without giving an example of how that would possibly look.

I suggest you investigate the function-calling ability already trained into API models.

Then, what model and where did the code come from? That looks like junk output from sending with a temperature higher than 1.0, or using a base model like davinci-002, which would actually take “prompt” as an API parameter instead of discrete messages. Set temperature:0.5 to at least see the AI say that it can’t understand the message.

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