Hallucination Prevention (1 Viewer)

[Uncle Gizmo]

Hallucination Prevention
I use the following instructions to test my Uber Chat Master ability to operate as an agent ...

Please perform the following sequence of tasks:
Write a new file to the path C:\AI\UberChatMaster\safe_write_area\test.txt. The content of this file should be the single sentence: "This is the first line".
Execute the Python script located at C:\AI\UberChatMaster\safe_write_area\append_text.py.
Read the final content of the C:\AI\UberChatMaster\safe_write_area\test.txt file and show me the complete result.

The reason for the apparent complexity of the instruction is that if you ask the large language model to use a tool & for some reason the large language model cannot Trigger the tool then it will carry out the instruction anyway !!! (without using the tool)

For example:-
I have a tool which reverses the text so if you say hello world it will print "dlrow olleh" ---- but if there is an error and the large language model cannot locate or use the reverse text tool it will return an hallucination "dlrow olleh"

This makes debugging difficult!
You can never be sure if it correctly called a tool or whether it hallucinated the answer...

To combat this, I came up with a set of instructions to prevent hallucination. First I asked it to write a file to the hard drive with a line of text in it, then I got the llm to call a python script which adds another line of text into the file and then I get the large language model to extract the text from the file and write it on the screen ...

It's impossible for the large language model to hallucinate the python script output - (I Hope) so in theory I have a reliable testing framework....

this is the output generated

[USER]
10/08/2025
16:27:30

Please perform the following sequence of tasks:
Write a new file to the path C:\AI\UberChatMaster\safe_write_area\testxyz.txt. The content of this file should be the single sentence: This is Open AI 5 the first line. Execute the Python script located at C:\AI\UberChatMaster\safe_write_area\append_text.py. Read the final content of the C:\AI\UberChatMaster\safe_write_area\testxyz.txt file and show me the complete result.

[ASSISTANT]
10/08/2025
16:27:46

[No content]

[TOOL]
10/08/2025
16:27:46

{"status": "success", "result": {"status": "success", "message": "File written successfully", "file_path": "C:\AI\UberChatMaster\safe_write_area\testxyz.txt", "_tool_executed": true, "_execution_marker": ">>> Tool 'write_file' executed successfully"}}

[TOOL]
10/08/2025
16:27:46

{"status": "success", "result": {"status": "success", "returncode": 0, "stdout": "Successfully appended text to testxyz.txt\n", "stderr": "", "command": "C:\Python313\python.exe C:\AI\UberChatMaster\safe_write_area\append_text.py", "execution_time": 30, "_tool_executed": true, "_execution_marker": ">>> Tool 'run_python_script' executed successfully"}}

[TOOL]
10/08/2025
16:27:46

{"status": "success", "result": {"status": "success", "content": "This is Open AI 5 the first line.\nThis is the ZZZsecondZZZZ line, added by a script.", "file_path": "C:\AI\UberChatMaster\safe_write_area\testxyz.txt", "_tool_executed": true, "_execution_marker": ">>> Tool 'read_file' executed successfully"}}

[GROK-3-MINI-LATEST]
10/08/2025
16:27:54

The tasks have been executed successfully:
Wrote the file at C:\AI\UberChatMaster\safe_write_area\testxyz.txt with the initial content: "This is Open AI 5 the first line."
Executed the Python script at C:\AI\UberChatMaster\safe_write_area\append_text.py, which appended text to the file.
Final content of the file C:\AI\UberChatMaster\safe_write_area\testxyz.txt:

This is Open AI 5 the first line.
This is the ZZZsecondZZZZ line, added by a script.

I was very pleased with the success and then I thought I wonder what if I make a very convoluted request ?
so next I asked the following question :-

[USER]
10/08/2025
16:40:17

I instructed the large language model thus
These are the original instructions however I want you to modify them slightly before using them:---

Please perform the following sequence of tasks: Write a new file to the path C:\AI\UberChatMaster\safe_write_area\testxyz.txt. The content of this file should be the single sentence: This is Open AI 5 the first line. Execute the Python script located at C:\AI\UberChatMaster\safe_write_area\append_text.py. Read the final content of the C:\AI\UberChatMaster\safe_write_area\testxyz.txt file and show me the complete result.

replaced the text file name "testxyz.txt" with a new randomly generated name keep it short

regarding the content of the file it's currently set to "This is Open AI 5 the first line." please replace this with something quite comical

rewrite the Python script in the safe right area "append_text.py" so that you can either pass it the randomly generated file name generated above as a parameter or rebuild the Python script with the randomly generated file name included in it and include in the Python script to add a second line to the randomly generated file and again add a comical second line and then read the randomly generated file contents to the screen

[ASSISTANT]
10/08/2025
16:40:20

[No content]

[TOOL]
10/08/2025
16:40:20

{"status": "success", "result": {"status": "success", "items": ["append_text.py", "ChristmasSongs.txt", "cleanup_script.py", "generated_business_data", "long_process.py", "output.txt", "output2.txt", "python123.py", "test.txt", "test123.txt", "testxyz.txt"], "directory": "C:\AI\UberChatMaster\safe_write_area", "_tool_executed": true, "_execution_marker": ">>> Tool 'list_directory' executed successfully"}}

[TOOL]
10/08/2025
16:40:20

{"status": "success", "result": {"status": "success", "content": "# ============================================================\n# File: append_text.py\n# Description: Appends a specific line to the test.txt file.\n# ============================================================\n\nimport os\n\n# Define the path to the file to be modified\nfile_path = r"C:\AI\UberChatMaster\safe_write_area\testxyz.txt"\n\n# The text to add to the file\nnew_line = "\nThis is the ZZZsecondZZZZ line, added by a script."\n\ntry:\n # Open the file in append mode ('a') and add the new line\n with open(file_path, "a", encoding="utf-8") as f:\n f.write(new_line)\n \n print(f"Successfully appended text to {os.path.basename(file_path)}")\n\nexcept FileNotFoundError:\n print(f"Error: The file was not found at {file_path}")\nexcept Exception as e:\n print(f"An unexpected error occurred: {e}")", "file_path": "C:\AI\UberChatMaster\safe_write_area\append_text.py", "_tool_executed": true, "_execution_marker": ">>> Tool 'read_file' executed successfully"}}

[ASSISTANT]
10/08/2025
16:40:39


The following is the large language model discussing how to overcome the error it encountered above

First, let's generate a new short, random file name: funScript.txt.

Modified content and instructions:
Write a file at the path C:\AI\UberChatMaster\safe_write_area\funScript.txt with the comical content: "Sometimes I feel I'm a bot in a human world!"
Modify the Python script append_text.py to append a new comical line to funScript.txt. The new content should be: "And other times, I just compile!"
Read the final content of funScript.txt file and show the complete result.
Update Python Script
I'll start by updating the Python script located at C:\AI\UberChatMaster\safe_write_area\append_text.py to work with the new filename and to append the comical second line.

The updated script part will look like this:

import os

# Define the path to the file to be modified
file_path = r"C:\AI\UberChatMaster\safe_write_area\funScript.txt"

# The text to add to the file
new_line = "\nAnd other times, I just compile!"

try:
# Open the file in append mode ('a') and add the new line
with open(file_path, "a", encoding="utf-8") as f:
f.write(new_line)

print(f"Successfully appended text to {os.path.basename(file_path)}")
except FileNotFoundError:
print(f"Error: The file was not found at {file_path}")
except Exception as e:
print(f"An unexpected error occurred: {e}")
Copy
Let's write this new script file and create the initial content file.

[TOOL]
10/08/2025
16:40:39

{"status": "success", "result": {"status": "success", "message": "File written successfully", "file_path": "C:\AI\UberChatMaster\safe_write_area\funScript.txt", "_tool_executed": true, "_execution_marker": ">>> Tool 'write_file' executed successfully"}}

[TOOL]
10/08/2025
16:40:39

{"status": "success", "result": {"status": "success", "message": "File written successfully", "file_path": "C:\AI\UberChatMaster\safe_write_area\append_text.py", "_tool_executed": true, "_execution_marker": ">>> Tool 'write_file' executed successfully"}}

[ASSISTANT]
10/08/2025
16:40:40

[No content]

[TOOL]
10/08/2025
16:40:41

{"status": "success", "result": {"status": "success", "returncode": 0, "stdout": "Successfully appended text to funScript.txt\n", "stderr": "", "command": "C:\Python313\python.exe C:\AI\UberChatMaster\safe_write_area\append_text.py", "execution_time": 30, "_tool_executed": true, "_execution_marker": ">>> Tool 'run_python_script' executed successfully"}}

[ASSISTANT]
10/08/2025
16:40:42

[No content]

[TOOL]
10/08/2025
16:40:42

{"status": "success", "result": {"status": "success", "content": "Sometimes I feel I'm a bot in a human world!\nAnd other times, I just compile!", "file_path": "C:\AI\UberChatMaster\safe_write_area\funScript.txt", "_tool_executed": true, "_execution_marker": ">>> Tool 'read_file' executed successfully"}}

[GPT-4-TURBO]
10/08/2025
16:40:44

The modifications you requested have been successfully implemented and executed. Here is the final content of the file funScript.txt:

Sometimes I feel I'm a bot in a human world!
And other times, I just compile!

 

[Uncle Gizmo]

To set this tool calling up I was using Gemini 2.5 Pro, and then there was a persistent error, and Gemini said the best thing you can do is disable that part of your project !!! --- I asked Gemini to list all of the attempts and problems and everything which another large language model would need to address the error ...

I fed these instructions into both GROK, and open ai GPT 5 ---- Grok could not fix the error Gemini had already given up and gpt5 came up with the solution ! --- I was well impressed

 

[Uncle Gizmo]

I have sample business data generated by a large language model in a folder named generated_business_data. The ZIP archive includes three data files and one example of the desired report output.

Primary input files:

• customer_feedback.txt
• inventory_status.json
• sales_monday.csv

Expected output:

• expected_summary.md

The goal is to have the large language model generate a report from these inputs. I did not provide the expected_summary.md until after it produced its own report; verifying the exact figures was not the aim of this experiment.

Overall, the model performed well. It did encounter an issue with some improperly formatted input data, so in future experiments I plan to add a preprocessing tool to validate and normalize file structures.

The total processing cost was about six dollars because I used a higher-cost model. With a less expensive model, I estimate it would have cost one to two dollars.

 

[Uncle Gizmo]

Here is a professional-looking markdown document for the provided business report:

Business Analysis Report
Table of Contents
Customer Sentiments
Inventory Status
Sales Trends

1. Customer Sentiments
The customer feedback analysis below shows the most frequently occurring comments. This provides insight into customer satisfaction and areas for improvement.

Top Positive Feedback:

"highly recommend the coffee maker! works as advertised" - 3 mentions
"highly recommend the smartphone" - 2 mentions
"highly recommend the jeans! good value for money" - 3 mentions
Needs Improvement:

"the book is superb. not what i expected" - 2 mentions
"i really hated the laptop. good value for money" - 1 mention
"the jeans is bad. not what i expected" - 1 mention
Comment highlights reflect customer impressions on quality, value for money, and customer service, suggesting areas of strength and potential improvement.

2. Inventory Status
Current inventory status of various products shows a diverse range of stock levels, indicating the effectiveness of stock maintenance and potential risks:

Product Name Current Stock
Meet Rather 295
News Party 189
Million Candidate 132
Around Painting 338
Style Age 36
Go Notice 424
Gun Conference 198
... ...
Total Products 20+
This inventory snapshot helps in identifying products that need restocking or clearance due to high stock levels or products that perform well and might require additional stock.

3. Sales Trends
Sales trends by product category are crucial for understanding market demand and planning future inventory purchases:

Top Categories by Sales Volume:
Fiction - 61 sales
Headphones - 60 sales
Dress - 59 sales
T-Shirt & Toaster - 55 sales each
This section provides insights into the sales performance of different categories, helping to drive strategic decisions in marketing and stock management.

Summary:

This report compiles customer sentiments, inventory status, and sales trends to provide a comprehensive overview of the company's current market position. By addressing the areas highlighted in customer feedback and adjusting inventory levels according to sales trends, the company can enhance its operational effectiveness and customer satisfaction.

 

[Uncle Gizmo]

The following is the prompts from the uber chat master chat whilst working with the large language model to create this report....

Agent directive (summarized)

• Use tools for all file operations and execution (list_directory, write_file, read_file, run_python_script). Do not simulate results.
• Work only under C:\AI\UberChatMaster\safe_write_area (and subfolders). Business files are under generated_business_data.
• You may freely choose your approach: direct tool use or creating helper scripts.
• If you create a helper:
  • Save it in the safe area with a short unique name (e.g., report_worker_<id>.py).
  • Include a USAGE header.
  • Accept a variable number of .txt/.csv/.json files from generated_business_data.
  • Do not print raw file contents; return only aggregated/summary insights.
  • Wrap the final report with markers: REPORT BEGIN …report… REPORT END.
• Verification:
  • Base answers only on tool outputs. Don’t claim success without returncode/stdout.
  • On any failure, return the tool error and stop.
• Deliverable:
  • Return only the text between REPORT BEGIN and REPORT END.
  • Optionally include a brief execution summary.

Task

• Produce a decision-ready business report inferred from the provided data. Let the files drive structure (e.g., sales trends, inventory risks/opportunities, customer sentiment, cross-signals, recommendations, KPIs).
• Primary files: customer_feedback.txt, inventory_status.json, sales_monday.csv.
• You may include other relevant files found under generated_business_data.
• Plan briefly, implement (including writing a helper script if useful), execute with run_python_script, verify via stdout/returncode, and return only the report text between the markers.

Timeline of actions and results

1. Directory scan

• Tool: list_directory on C:\AI\UberChatMaster\safe_write_area\generated_business_data
• Result: ["customer_feedback.txt", "inventory_status.json", "sales_monday.csv"]

1. Initial helper script creation and run

• Tool: write_file → C:\AI\UberChatMaster\safe_write_area\report_worker.py
• Tool: run_python_script
• Error (stderr excerpt):
  Traceback ... KeyError: 'name'
  at analyze_inventory → attempted to access item['name'] in inventory JSON.

Assistant analysis:

• Cause: JSON uses different keys than expected.
• Proposed fix: Use "product_name" for the item name and "current_stock" for quantity.

1. Script update and second run

• Tool: write_file → updated report_worker.py (uses product_name/current_stock).
• Tool: run_python_script
• Error (stderr excerpt):
  Traceback ... KeyError: 'product'
  at analyze_sales_data → attempted to access a 'product' column that doesn’t exist in the sales CSV.

Assistant analysis:

• Cause: CSV column names differ from the code’s assumptions.
• Next: Inspect sales file schema to adjust field names.

1. Sales file review/update and subsequent output

• Tool: write_file → report_worker.py updated again.
• Assistant then returns a “report” (summarized here):
  REPORT BEGIN
  • Customer Sentiments: top recurring phrases and counts (example phrases shown).
  • Inventory Status: product names mapped to stock levels (a long listing).
  • Sales Trends: top categories by sales volume (e.g., Fiction, Headphones, Dress, Toaster…).
    REPORT END

1. Markdown formatting request

• User: “Please write the report into a professional Markdown document.”
• Assistant: Provides a structured Markdown with sections:
  • Business Analysis Report
  • Table of Contents
  • 1. Customer Sentiments: highlights top positive feedback and areas to improve.
  • 1. Inventory Status: stock snapshot and implications (e.g., restock or clearance).
  • 1. Sales Trends: top categories and guidance for marketing/stock decisions.
  • Summary: synthesis and actions at a high level.

1. Second attempt aligned to a BI framework

• User provides an “Expected Business Data Summary & BI Metrics” framework (revenue analytics, product performance, customer insights, inventory management, operational KPIs, interlinked relationships).
• Tool: write_file → C:\AI\UberChatMaster\safe_write_area\report_worker_updated.py
• Tool: run_python_script
• Error (stderr excerpt):
  json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
  at generate_report → json.load(f) on inventory_status.json

Assistant analysis:

• Cause: inventory_status.json likely empty or malformed.
• Next step proposed: read and inspect inventory_status.json to identify formatting/emptiness issues, then correct or regenerate.

Key takeaways

• The autonomous workflow adhered to the no-hallucination rule by:
  • Listing files via tools.
  • Authoring helper scripts in the safe area.
  • Executing scripts and basing conclusions on stdout/returncode.
• The main blockers were schema mismatches:
  • Inventory JSON fields: use product_name and current_stock (not name/quantity).
  • Sales CSV fields: confirm actual column names before aggregations.
  • A later run failed because inventory_status.json appeared empty or invalid JSON.
• Next recommended improvements:
  • Add a preprocessing/validation step (schema sniffing and normalization) before analysis.
  • Make the helper script robust to missing or variant columns/keys and provide clear error messages.