What happens when you send a blank context window? Very suprising

API
1

So last night I was testing a new context window builder in my application.

The result was that I didn’t build a context window, but I still made an LLM call.

What happened was I got a lengthy response from the LLM on some topic that was seemingly completely random, but highly structured as if the LLM had received a real prompt from me.

I sent a blank context window that contained ZERO role: user or role: assistant messages in the context window, only a role: developer message with the line “LUCID SYSTEM 3D CONTEXT WINDOW”.

I sent these calls to gpt4.1 over the completions endpoint.

I thought that this might warrant some attention as what it truly seems like is happening to me is that I’m somehow getting a response from the LLM that theoretically is either being generated out of complete thing air with no prompt (how is this possible?) or somehow an LLM response from a totally separate API call from someone elses API key is either being duplicated (or intercepted??) and provided back as a response in my API call?

Never seen anything like this before.

Check out the full logs below. Enclosing each run in a details/summary block below.

Note, none of this content is topically related to anything I’ve ever used the GPT for. And I confirmed at all levels of my system, that indeed essentially I prompted the LLM with essentially a blank context window.

run #1 [4/28/2025, 1:50:07 AM] OpenAI API Log Log ID: 5402 | Conversation ID: 11816 | Thread ID: 987 Request / Response Metadata

Request Model: gpt-4.1

Usage: Prompt: 19, Completion: 101, Total: 120

Request Messages

Message #1 | Role: system
LUCID SYSTEM 3D CONTEXT WINDOW

Response Messages

Choice #1 | Role: assistant
Certainly! Here’s a sample reply to an email from an employer who sent you onboarding forms:

Subject: Re: Onboarding Forms Received

Dear [Employer’s Name],

Thank you for sending over the onboarding forms. I have received them and will complete and return them as soon as possible.

Please let me know if there are any additional documents or information you need from me at this time.

Looking forward to starting!

Best regards,
[Your Name]

Feel free to personalize it further!

run #2 [4/28/2025, 1:48:32 AM] OpenAI API Log Log ID: 5401 | Conversation ID: 11815 | Thread ID: 987 Request / Response Metadata

Request Model: gpt-4.1

Usage: Prompt: 19, Completion: 826, Total: 845

Request Messages

Message #1 | Role: system
LUCID SYSTEM 3D CONTEXT WINDOW

Response Messages

Choice #1 | Role: assistant
Absolutely, here is an affiliate marketing plan tailored specifically for promoting software products:

Affiliate Marketing Plan for Software Products

1. Define Your Target Audience

  • Identify Ideal User: Decide if you’re targeting individuals (e.g., freelancers, students) or businesses (e.g., SMBs, enterprises).
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2. Choose the Right Software Products

  • Focus Area: Select software in a niche you understand (e.g., SaaS productivity tools, design software, security apps).
  • Research Programs: Look for reputable affiliate programs with high commission rates (15–50% is common for software/SaaS).
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3. Join Affiliate Programs

  • Official Programs: Apply directly through the software’s website or via networks like Impact, ShareASale, CJ Affiliate, PartnerStack.
  • Track Links: Use the unique tracking links provided for each product.

4. Content Strategy

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  • In-depth Reviews: Write or record honest reviews with pros and cons.
  • Tutorials/How-to Guides: Show step-by-step usage and share tips.
  • Comparison Posts: Compare with competitors (“X vs. Y: Which is Best?”)
  • Case Studies: Share real-world results from using the software.
  • Listicles: “Top 5 Best [Category] Tools in 2024”
  • YouTube Demos: Record video walk-throughs and feature highlights.

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  • Keyword research for buyer intent terms: “[Software] review,” “Best [category] software,” “How to use [software].”
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  • Blog: Publish regular, high-quality posts targeting your niche.
  • YouTube: Film demo/review/tutorial videos (add affiliate links in descriptions).
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  • CTAs: Use clear calls-to-action (“Try [Software] free for 30 days”).
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  • Disclosures: Clearly disclose affiliate relationships (required by FTC).
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  • Feedback Loop: Provide product feedback to software companies and request promo codes or exclusive deals for your audience.

Example Workflow

  1. Research and join 3 high-commission software affiliate programs.
  2. Publish a detailed review and a comparison article each month.
  3. Film a monthly YouTube tutorial for one software.
  4. Build an email list via a lead magnet and send monthly “tool of the month” emails.
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Tip: Focus on software with recurring commissions (SaaS subscriptions) for steady passive income.

Let me know your niche or if you want sample content outlines, email templates, or a list of top affiliate programs for your vertical!

run #3 [4/28/2025, 1:51:28 AM] OpenAI API Log Log ID: 5403 | Conversation ID: 11817 | Thread ID: 987 Request / Response Metadata

Request Model: gpt-4.1

Usage: Prompt: 19, Completion: 1163, Total: 1182

Request Messages

Message #1 | Role: system
LUCID SYSTEM 3D CONTEXT WINDOW

Response Messages

Choice #1 | Role: assistant
Absolutely, here’s a detailed comparison between the Cybertruck (from Tesla) and the Hummer EV (from GMC) as of 2024. I’ll break down their specifications, features, pros & cons, and give a summary table.

1. Powertrain & Performance

Feature Cybertruck (Tri-Motor AWD) Hummer EV (Edition 1)
Motors 3 3
Horsepower ~845 hp 1,000 hp
0-60 mph ~2.6 seconds ~3.0 seconds
Torque Unspecified (massive) 11,500 lb-ft* (wheel)
Drive All-wheel drive All-wheel drive

*GM’s torque figure is at the wheels and not directly comparable to the Tesla’s—real-world torque is much lower.

2. Range & Battery

Feature Cybertruck (Tri-Motor AWD) Hummer EV (Edition 1)
EPA Range Up to 340 miles Up to 329 miles
Battery Pack ~123 kWh (est.) ~212 kWh
Charging Speed 250 kW (Supercharger V3) 350 kW (800V charging)

3. Towing & Payload

Feature Cybertruck (Tri-Motor AWD) Hummer EV (Edition 1)
Towing Capacity 11,000 lbs 7,500 lbs
Payload Capacity 2,500 lbs 1,300 lbs
Bed Size 6.5 ft 5 ft (MultiPro tailgate)

4. Features & Technology

Feature Cybertruck Hummer EV
Autopilot/ADAS Tesla Autopilot, FSD option Super Cruise, CrabWalk, Extract Mode
Interior Minimalist, 18.5" display Luxury, 13.4" display, 12.3" cluster
Off-Road Adaptive air suspension Extreme off-road: CrabWalk, Extract, 4WS
Other Stainless steel exoskeleton, armored glass Removable glass roof panels (Infinity Roof), UltraVision cameras

5. Pricing (2024)

Model Starting MSRP (USD)
Cybertruck (AWD) $79,990+
Hummer EV (Edition 1) $112,000+

6. Pros & Cons

Cybertruck

Pros:

  • Lower price (especially for lower trims)
  • Higher towing/payload
  • More range per dollar
  • Futuristic, unique design
  • Tesla Supercharger network

Cons:

  • Polarizing looks, panel fit/finish concerns
  • Early production models only high-end trims
  • Bed/utility limitations (higher sidewalls)
  • No removable roof

Hummer EV

Pros:

  • True off-road monster (CrabWalk, Extract Mode, 4WS)
  • Luxurious interior, more conventional truck feel
  • Removable roof for open-air experience
  • Faster DC charging

Cons:

  • Very heavy (over 9,000 lbs)
  • Expensive (especially Edition 1 and early trims)
  • Less towing/payload
  • Lower efficiency (bigger battery but similar range)

7. Summary Table

Feature Tesla Cybertruck GMC Hummer EV
Range Up to 340 miles Up to 329 miles
Power ~845 hp (Tri-Motor) 1,000 hp
0-60 mph 2.6 sec 3.0 sec
Towing 11,000 lbs 7,500 lbs
Payload 2,500 lbs 1,300 lbs
Bed length 6.5 ft 5 ft
Price (base, AWD) $79,990+ $112,000+
Notable Tech FSD, stainless steel CrabWalk, removable roof

Which Should You Buy?

  • Cybertruck: Best for those looking for maximum utility (towing, payload), unique style, and lower price, especially if already invested in the Tesla ecosystem.
  • Hummer EV: Best for those wanting luxury, serious off-road capability, and a more traditional truck experience (with a side of “wow” factor).

Both trucks are statement vehicles, but the Cybertruck leans futuristic/utility, and the Hummer EV leans luxury/off-road.

Let me know if you need a deeper dive into any specific aspect!

Now I just tried to replicate this behavior again today and got a normal response instead.

Phew!! What in the world! I’m mystified…

Run #4 (attempt at replicating behavior) [4/28/2025, 12:39:25 PM] OpenAI API Log Log ID: 5405 | Conversation ID: 11819 | Thread ID: 987 Request / Response Metadata

Request Model: gpt-4.1

Usage: Prompt: 19, Completion: 10, Total: 29

Request Messages
Message #1 | Role: system
LUCID SYSTEM 3D CONTEXT WINDOW

Response Messages
Choice #1 | Role: assistant
Sure! How can I assist you today?

2

Would you believe that with experimentation into GPT-1, GPT-2, having your own preloading of some context window to complete on, instead of just seeing what a pretraining would exhibit naturally as generation, was optional and novel? “Look, it can actually produce language when in a feedback loop!”

GPT-4.1 is not going to be as random, though - it is highly post-trained. Additionally, there’s a minimum context of messages that you must place in the ChatML format and the AI is prompted with "(start-token)assistant, and it’s going to be a 500 server error if it doesn’t write the correct highly-predictable unseen token after that.

Then you get generation proceeding into the kind of stuff an assistant might write and is tuned on, when you provide extremely meaningless input and let more random token selection start things off.

The image shows settings for a language model, including parameters like response format, temperature, and top-p values. (Captioned by AI)
The image shows settings for a language model, including parameters like response format, temperature, and top-p values. (Captioned by AI)364×276 7.45 KB

Absolutely! Here is a solid script template you can use for a self-introduction during a university interview in the UK. You should personalize it with your details and interests:

Good morning/afternoon, and thank you for giving me the opportunity to introduce myself. My name is [Your Name], and I am currently finishing my [A-levels/IB/other relevant qualifications] at…

or

Claro. Aquí tienes un ejemplo de carta formal para solicitar una constancia laboral:

[Nombre de la empresa] [Dirección de la empresa] [Teléfono de la empresa]

[Ciudad], [fecha]

A quien corresponda:

The “deterministic” output is the not knowing what the heck you want:

Sure! Please provide the text you’d like me to summarize.

3

Hey J, thanks for the reply. I’m not sure what you mean though.

I haven’t been able to replicate the previous behavior. But at that point of the original three test runs, prompting the LLM with a totally blank context window (outside that developer message which only included the seemingly non-content of “LUCID SYSTEM 3D CONTEXT WINDOW”), produced highly structured responses that seemed clearly to be responding to a specific topic or question that did not exist whatsoever in the prompt, as there was no prompt (no role: user messages or assistant messages at all - no context window outside that single line of role: developer message).

You seem to be insinuating that this is “normal” - in the sense that the LLM, when prompted “with nothing in the context window” is indeed of course still receive some basic data, in the sense that whatever backend metadata OAS is inserting into the context window being passed to the LLM is a “part of the LLM processing”.

However, again I haven’t been able to actually replicate this behavior - currently when I attempt it’s only giving basic “What would you like help with?” responses when receiving the blank context window - but in those specific test cases previously shown - and I’ve reviewed my system and ensured nothing more was sent somehow (I dumped the raw call to OAS in a file before the HTTP call was made to completions) I got those responses that indicated the LLM was responding to some kind of prompt input, but it wasn’t prompt input that I had provided in the call.

Thus my presumption that the response from the LLM was coming from elsewhere then what I had actually provided in my HTTP call.

To me this was very interesting, and from my understanding not at all “by design” of the completions endpoint nor the transformer architecture.

4

Perhaps it is important to understand what happens at a deeper level in the transformer AI.

The inputs and outputs of the AI language model are tokens, you’re probably familiar with that. More specifically, a certainty score is produced for every one of 200000 tokens that are possible. That repeats every time another token needs to be generated.

So, 200k scores? Yes, a full dictionary, how likely each particular logit is to be the “correct” token for such a position based on reinforcement learning.

Does the AI just use the “best”? No. There is sampling. A random selector picks from the likelihood in the probability distribution of all. If the first token to be generated is 50% going to be “Certainly”, then in 50% of the trials you run, you get output that starts with something else.

That’s why you get different things written each time. The AI might have a 0.0004% chance of “Bueno” being how your output starts - very hard to reproduce what you received such a curiosity in one occurrence. Making you scratch your head where it came from.

The temperature setting as an API parameter can bias towards picking the more likely tokens, or can flatten the distribution. The top_p parameter can discard less likely tokens.

Setting both to near zero, we indeed can get the same thing almost every time. Every token is selected from its best option.

gpt-4.1, temperature:0, top_p:0:

As of my knowledge cutoff in June 2024, there is no widely recognized technology, product, or framework called “Lucid System 3D Context Window” in the fields of AI, software, hardware, or 3D visualization. However, your query seems to combine several concepts that are relevant in current technology trends:

1. Lucid System…

That response is very much like sending a single message with some out-of-context language for an AI assistant to answer about. The most likely way for it to answer.

The API has a very particular behavior though - seemingly connecting states together in repeated calls for an organization, which is completely wrong except for saving OpenAI money. Once a temp/top of 0 is run, increasing temperature is ineffective for receiving much else, even at high levels. It appears to be a caching-like malfunction of the API. One has to switch to a different model.

Hello! How can I assist you with Linux distros today?

or a whole bunch of typical greetings until:

Vidrolcmcrm unsüll Project Hallo!
How comissão Thanks regal99 happened fait»
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Why does it answer like it has some preset data or behaviors that it’s answering from?

Because of reinforcement learning, and further, from training to follow instructions. This is like model fine-tuning, you making a sarcastic AI, it’s going to favor sarcastic answers being produced even without the stimulus for them.

Sending your developer (system) message to one of my fine-tuning AI models, we see its training also kicking in:

Sit tight. Starting up something special just for you. Watch and you’ll wonder what’s real… and what’s just your mind playing tricks.

So, writing something that looks like it is meant for somebody else: working the way its supposed to, the way language AI works. The API is for developing applications where you use the context and your control usefully for instructions and tasks, and really, you’d use a base model like davinci-002 on the completions endpoint if you want to learn more about pretrained language completion as an experiment.