Most developers are still using GPT-4-style prompting on GPT-5… and that’s exactly why they’re getting worse results. GPT-5 isn’t failing — your approach is. Here’s the real problem: Hallucinations Context drift Inconsistent reasoning These aren’t just “model issues.” They’re context management failures. Even research and real-world feedback show that hallucinations often happen when models lose or misinterpret context—not just because they lack intelligence. � LinkedIn +1 So instead of “prompting better,” I did something different: I stopped prompting. I built a 45-line context engine that: Structures inputs Controls context flow Anchors reasoning Reduces ambiguity Key shift: From prompts → systems From instructions → architecture From outputs → pipelines This is where the real leverage is. Because once context is structured properly: Hallucinations drop Outputs stabilize Reasoning becomes predictable Even advanced models perform better when guided by structured frameworks rather than raw prompts. � Medium The future isn’t prompt engineering. It’s context engineering + system design. 👉 Read the full breakdown here: buildwithclarity.hashnode.dev/i-stopped-prompting…
Apurv Julaniya
Boost your skills and life
I agree with the shift towards context engineering and system design over traditional prompting. By focusing on structuring inputs and controlling context flow, we can reduce hallucinations and improve output stability. This approach offers more reliable results, especially with advanced models like GPT-5. It's a game changer for developers.
Strong shift in perspective.
The idea that hallucinations are primarily a context management problem rather than just a model limitation is the key takeaway here. Most people are still trying to “fix” outputs at the prompt level, while ignoring how poorly structured the inputs actually are.
The 45-line context engine is interesting because it forces discipline:
explicit context boundaries
controlled information flow
reduced ambiguity before generation
That’s essentially moving from trial-and-error prompting to deterministic system behavior.
One question worth pushing further: How do you evaluate whether your context engine is actually reducing hallucinations consistently across different tasks, not just in isolated cases?