Reference
Stop doing
Each item is something practitioners in the 2026 threads specifically called out, with the evidence.
| Stop | Why | Evidence |
|---|---|---|
| Learning prompt engineering as a career | Called "never a real role" by an experienced engineer; context engineering and evals replaced it | r/LLMDevs, Jul 2026 (13 pts): https://www.reddit.com/r/LLMDevs/comments/1v7z7e7/comment/p052pym/ |
| Chasing frameworks (LangChain, LlamaIndex, the agent framework of the month) | "There is no roadmap anymore" for agent tooling; it changes too fast. Learn system design instead. Several production teams moved to minimal custom code | r/learnmachinelearning, Jul 2026 (29 pts): https://www.reddit.com/r/learnmachinelearning/comments/1uqymz7/comment/owc6cz1/ |
| Treating classical model tuning as the whole job | A 10-year MLE says 98% of those use cases went to cheap LLM API calls; the middle of the market is squeezed | Same thread |
| A portfolio of unevaluated demos | Interviews ask how you measured hallucinations and why you chose the design. "Chat with your PDF" with no eval set reads as unfinished | 15-interviews write-up (447 pts): https://www.reddit.com/r/deeplearning/comments/1swxb2k/ |
| Believing MLOps and pipelines are dead because an agent can write them | 125-upvote reply: the tools change, the concepts stick; banks and autonomous-vehicle companies still run on these pipelines and on people who understand them | r/learnmachinelearning, Oct 2026: https://www.reddit.com/r/learnmachinelearning/comments/1wwa0yj/comment/pdiwbwt/ |
| Not reading the code your assistant writes | 1,500-upvote hiring thread: developers who cannot tell bad AI decisions from good become a net negative | r/ExperiencedDevs, Sep 2026: https://www.reddit.com/r/ExperiencedDevs/comments/1wql3g2/comment/pc51suc/ |
| Months of math before touching a model | The common regret in 2025 to 2026 learner write-ups; practitioners say build first, fill math gaps on demand | See how-to-study appendix |
| Bouncing between resources | Raschka and Karpathy both say pick one and finish it | https://www.dwarkesh.com/p/andrej-karpathy |
| Reading every new arXiv paper | Spend time on breakthrough papers and skim derivatives; some research areas "might only have three papers you need to read" (Lambert) | https://lexfridman.com/ai-sota-2026-transcript |
| Fine-tuning to add knowledge | Production teams: RAG for knowledge, fine-tuning for behaviour and format | r/Rag, Sep 2026: https://www.reddit.com/r/Rag/comments/1wbhsdi/comment/p8pycuc/ |
| Claiming "shipped" for things nobody uses | Interviewers call it a red flag | https://www.reddit.com/r/LLMDevs/comments/1v7z7e7/comment/p03i3np/ |
| Evaluating by "vibes" | An interviewer's listed red flag, with unbounded agent loops and proposing a cheaper model before measuring where tokens go | https://chiraghasija.cc/posts/how-to-interview-ai-engineer-role-2026/ |
| Promising yourself "expert in 6 months" | Reddit timelines for self-taught transitions in 2025 to 2026 ran 18 months to 3 years. Anything faster is marketing | Research caution note; https://www.reddit.com/r/learnmachinelearning/comments/1pzcw2y/ |
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