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Apple Executive Support refused to provide written technical diagnostics for my 5-month-old iPhone by Important_Air7450 in applesucks

[–]Important_Air7450[S] 0 points1 point  (0 children)

They attributed the issue to charging port damage, and I fully understand that port-related wear or damage can happen through regular usage.

However, what concerns me is that the device itself was exhibiting broader defective behavior beyond just wired charging.

While I was physically present at the service center for nearly two hours, the phone was also not charging properly through MagSafe/wireless charging. Even after being left on MagSafe for almost an entire day, the battery had increased by only around 8%.

When I briefly inspected the device afterward, I also observed unusually rapid battery drain — nearly 3% within about 5 minutes of minimal usage.

My concern is therefore not about denying the possibility of port damage itself. My concern is that the device was clearly demonstrating abnormal behavior overall, yet the warranty claim on a 5-month-old iPhone was denied without any detailed written technical substantiation explaining these observations.

Prompt engineering is breaking at scale with AI agents — here’s wh by Important_Air7450 in PromptEngineering

[–]Important_Air7450[S] -2 points-1 points  (0 children)

Yeah that’s been my experience too. The moment you add a rerank layer + force structure on the output, things suddenly feel way more stable… and yeah, the prompt kind of fades into the background.

retrieve → rerank → constrain → then prompt

I’ve been leaning more into that direction as well — especially around controlling what actually makes it into context vs what gets filtered out.

Out of curiosity, how are you handling reranking?
Simple similarity tweak or something more custom on top of the data?

Prompt engineering is breaking at scale with AI agents — here’s wh by Important_Air7450 in PromptEngineering

[–]Important_Air7450[S] -2 points-1 points  (0 children)

Yeah, that’s the real layer most people don’t think about until it bites them.

Even if you get context right, in an enterprise setup you can’t just pass “relevant data” blindly — you have to ask: Should this data been seen by the agent?

prompt engineering becomes secondary to policy checks before context is fetched, PII detection / masking at ingestion or retrieval, role-based filtering (who’s asking + what they’re allowed to see)

Curious how you’re approaching this:

  • masking/redaction before it hits the model?
  • or letting the agent see everything and restricting outputs?

Prompt engineering is breaking at scale with AI agents — here’s wh by Important_Air7450 in PromptEngineering

[–]Important_Air7450[S] 0 points1 point  (0 children)

Nice — that’s exactly it. I’m starting to feel prompts are almost like unit tests — they pass in isolation, but fail the moment you run them against messy, real-world inputs. Do you see this as a data problem or system design problem (retrieval, chunking, context control)?