One seemingly innocent question — "Which neighborhood should I live in?" — can spiral into something far bigger. That's exactly what happened to a developer who, as a recent article on Habr describes, simply wanted to ask neural networks for apartment-hunting advice. The result? A full-blown research pipeline with 1,197 tests. Source
The story begins with a practical need: finding a good place to live. Faced with the chaos of rental listings, conflicting advice from friends, and the sheer noise of online forums, the developer turned to AI. Because why not? Large language models (LLMs) have read the internet; surely they can tell you whether to choose the suburbs or a downtown loft. But the answers were... let's say, inconsistent. Sometimes ChatGPT would recommend a quiet residential area, other times a bustling district with nightlife — depending on how the question was phrased, even by a few words.
This is the classic problem with generative AI: it's fluent but not always reliable. It can confidently describe a neighborhood that doesn't exist, or suggest checking a website that has been closed for years. And each query is a singular black box — you don't know why you got that answer, or whether a different prompt would have produced a completely different suggestion. For someone who just wants a decent apartment, this is frustrating. For the curious developer, it's an invitation to build a rigorous experiment.
The Problem: Subjective, Inconsistent AI Advice
Apartment hunting involves many variables: budget, commute time, safety, schools, proximity to grocery stores, nightlife, greenery, and future resale value, to name a few. Any single LLM might handle a few of these, but rarely all of them consistently across different prompts. The author observed that changing the tone of the query, the order of listed criteria, or even the language could dramatically shift the recommendation. These aren't just random wobbles — they point to deeper issues in how models interpret constraints and weight trade-offs.
Moreover, the AI's advice often came without transparent reasoning or sources. It would say "this area is perfect for young professionals" but never explain which data it used to reach that conclusion. In a decision as important as housing, blind trust is dangerous. The author needed not just one answer, but a way to figure out systematically which AI (or which prompting strategy) gave the most sensible, usable guidance.
The Solution: A 1,197-Test Evaluation Pipeline
Instead of manually comparing a handful of responses, the author decided to do what any engineer would do: automate the evaluation. The article describes building a pipeline that ran 1,197 tests — essentially a small-scale research project. While the exact architecture isn't fully detailed in the article, we can infer the key components from how such pipelines are typically constructed.
First, you need a test set. In this context, the author likely generated a variety of apartment-hunting scenarios: different budgets, family sizes, job locations, lifestyle preferences. Each scenario serves as a "problem" to present to the AI. Then, you need a way to score the responses. That could be done by human reviewers, but with 1,197 tests you'd want an automated rubric. For example, does the recommendation mention proximity to public transit? Does it avoid suggesting known bad neighborhoods? Does it provide actionable advice rather than vague platitudes?
The author probably also tested multiple AI models or different versions of the same model — and maybe different prompt engineering techniques like few-shot examples, role-playing ("You are a local real estate agent"), or providing a checklist of criteria. This is where the magical number 1,197 comes from: a combinatorial matrix. Imagine 10 scenarios × 3 models × 2 personas × 20 prompt variations, plus some control cases. The exact math we don't know, but the point is that the author created a structured, repeatable experiment rather than chat-side ad hoc guesses.
To build such a pipeline, you need access to the models' APIs. Tools like the OpenAI API for ChatGPT, Anthropic's Claude, or open-source models hosted on platforms like Hugging Face are all accessible. This is where a data integration platform becomes valuable. For instance, ASI Biont supports connecting to ChatGPT via API — learn more at asibiont.com/courses. Having a unified way to call different models and record the outputs is crucial for reproducibility.
What the Tests Revealed: Objective Answers to Subjective Questions?
The results of this experiment, while not fully enumerated in the article, likely reveal several important patterns. First, all AIs are not created equal. Some models are significantly better at handling multifaceted, practical advice. They don't just list a neighborhood; they explain trade-offs and ask clarifying questions. Others tend to produce generic answers that could apply to any city in the world.
Second, prompt design matters enormously. The author's tests probably showed that telling the AI "You are a local apartment hunter" or "Pretend you are moving to this city for a tech job" yields more grounded responses than a neutral query. This is a well-known phenomenon in LLM research: the model's responses are sensitive to the "persona" you assign. Third, repetition is key. The same model with the same prompt can give different answers due to temperature settings. Running each test multiple times and averaging or taking the majority vote would be a logical step.
But perhaps the most striking insight is that AI, as of 2026, still struggles with context. No model can truly understand the subjective concept of "home". It can aggregate public data about crime rates, school rankings, or real estate pricing, but it can't know that the specific street you liked has a noisy bar below the apartment you're considering. That's where the human must remain in the loop.
Practical Lessons for Your Own AI-Driven Research
The author's odyssey from a simple question to a multi-thousand-test pipeline isn't just an amusing story — it's a blueprint. Here's what anyone can take away:
- Never trust a single AI answer. Fluent doesn't mean factual. Always cross-check with multiple sources.
- If the AI gives conflicting advice, it's not just a glitch. It reveals how your question's framing affects the output. Try rephrasing or adding constraints.
- Build a mini-test suite, even for casual tasks. You don't need 1,197 tests, but even 20 different scenarios can show you which AI or prompt works best for your needs.
- Automate what you can. If you're regularly using AI for research — whether for apartment hunting, stock analysis, or blog writing — don't manually copy-paste outputs. Use an integration layer to send queries and collect results in a structured way.
- Look for sources in the output. The most helpful AI responses cite datasets or link to official resources like public transport maps or city planning sites. If the AI doesn't provide sources, treat its advice as a starting point, not a conclusion.
The software world is full of stories about a simple hack that turned into a company. This isn't one of them — it's a story of a curious person who asked a practical question and ended up doing rigorous quality assurance on a piece of technology that many of us rely on without thinking. The 1,197 tests represent not just a number, but a mindset: AI is not a magic oracle; it's a tool that needs to be tested, evaluated, and understood.
The Bigger Picture: LLMs as Untested Interns
If you think about it, asking ChatGPT to recommend a neighborhood is like asking a brilliant but inexperienced intern to do a task. The intern might have read all the briefing documents (the training data), but they haven't walked the streets, felt the vibe at 11 PM, or seen the state of the laundry room. The only way to know if the intern is any good is to give them several tasks and grade them. That's precisely what the author did.
The consequence of this experiment may not be earth-shattering, but it echoes a broader trend. As AI becomes embedded in everyday decisions — from apartment rentals to medical advice — the need for evaluation frameworks grows. Companies like Anthropic, OpenAI, and Google all have internal evaluation suites, but individual users are often left in the dark. This article demonstrates that even a solo developer can build a credible evaluation harness with minimal resources.
One could argue that the real find here isn't a perfect apartment, but the Pipeline. By sharing the process, the author gives others a template to run similar tests for their own use cases. That's the kind of practical, hands-on knowledge that the internet thrives on.
Final Take: Ask Better Questions, Build Better Systems
In the end, the author got more than an apartment recommendation. They got an insight into the nature of AI reliability. The next time you ask an AI something — whether it's "where to live" or "how to invest" — remember that the model is guessing based on patterns, not experiencing reality. Your job is to grill it, stress-test it, and cross-examine it. And if you're technically inclined, you too can set up a small-scale testing loop to find which AI actually gives the best advice for your situation.
We've seen this narrative before: a simple question leads to a deep dive, and the journey becomes more valuable than the destination. For apartment hunters and AI enthusiasts alike, the lesson is clear: don't settle for one answer — build a way to get many, and then think critically about all of them.
This story also offers a glimpse into the future of human-AI collaboration. It's not about replacing human judgment; it's about augmenting it with systematic validation. The author's 1,197 tests might seem obsessive, but in a world where AI influences our daily choices, a little obsessive rigor is exactly what we need.
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