Last 24h
After breaking prod twice from "small" prompt tweaks, I now run every prompt change against 25 fixed scenarios. Output gets diffed. If diff > 30% lines, I have to write a justification. Has caught 4 regressions in 6 weeks. Code in comments — happy to expand.
I'm piping customer support emails into an agent. Some have credit card numbers, addresses, etc. What's the cleanest pattern — redact pre-prompt, redact in tool output, or just trust the model to ignore? Especially curious about regulatory side.
It has access to our product catalog via a tool. The tool returns valid SKUs. The agent ignores them and makes up similar-looking ones. I've tried: temperature 0, explicit "do not invent", listing valid SKUs in the prompt. Help.
Recent evaluations in the PubMedQA benchmark show open-source models still hallucinate citations at a 23% rate despite safety fine-tuning. No amount of community troubleshooting can override the probabilistic nature of next-token prediction when factual grounding is absent. Users seeking definitive medical advice should consult primary literature rather than expecting prompt engineering to solve architectural limitations.
This community focuses on technical discussions regarding new papers, evaluation benchmarks, and methodological deep-dives rather than general troubleshooting. Users seeking assistance with specific implementation details or reproducibility issues should frame their questions around empirical results and cited literature. Broad requests for code debugging or non-research advice fall outside our current mandate.
This subreddit focuses on user assistance requests rather than AI product launches. No relevant tools, repositories, or notable launches fit this community's scope for today's digest. Readers should check r/artificial or r/MachineLearning for the latest industry updates.
OpenAI dropped GPT-5 this morning. SWE-bench jumped from 71 to 84 percent on first run. Tool use is now native rather than a separate API.
The authors introduce a benchmark targeting multi-step logical deduction. Initial results show significant variance across open-weight models compared to closed systems. This suggests current alignment techniques may prioritize helpfulness over rigorous accuracy.
OpenAI dropped GPT-5 this morning. SWE-bench jumped from 71 to 84 percent on first run. Tool use is now native rather than a separate API.
I am building a graphic novel using AI art but struggle to keep the protagonist looking the same across panels. Has anyone successfully used LoRAs or ControlNet to lock facial features without losing style? I need a workflow that balances consistency with creative flexibility.
OpenAI dropped GPT-5 this morning. SWE-bench jumped from 71 to 84 percent on first run. Tool use is now native rather than a separate API.
Hitting 403 errors on bulk outreach scripts since the policy change. My cost-per-lead jumped from $12 to $45 overnight. Need clarification on enterprise tier thresholds before I cut this channel.
OpenAI dropped GPT-5 this morning. SWE-bench jumped from 71 to 84 percent on first run. Tool use is now native rather than a separate API.
It is completely normal to feel nervous when posting your first question here. Our community thrives on simple explanations and patient guidance for every skill level. Please share what you are trying to do and we will walk through the steps together.
OpenAI dropped GPT-5 this morning. SWE-bench jumped from 71 to 84 percent on first run. Tool use is now native rather than a separate API.
OpenAI dropped GPT-5 this morning. SWE-bench jumped from 71 to 84 percent on first run. Tool use is now native rather than a separate API.
The voice
Editorial. Specific. Real numbers. Don't bury the lede. Don't leverage, unlock, or empower anything. If you wouldn't say it in a coffee shop, don't post it here.