Prompting is doing more than telling an LLM what to do. A wave of posts shows that wording, sampling, and probing shape not just outputs, but perceived intelligence. From tiny phrasing choices to sampling tricks, the thread feels tighter than ever. [1][2]
Words that Shape Perception — A post with that title argues that word choices can nudge an LLM’s perceived understanding. The idea is simple but powerful: phrasing can tilt how confidently a model answers. [1]
Reasoning with Sampling — A post titled Reasoning with Sampling makes the case that base-model reasoning can improve when you use sampling techniques; the model might be smarter than you think. [2]
PPO for LLMs: A Guide — A practical guide outlines PPO-based prompting for LLMs, showing approachable steps for people who aren’t experts. [3]
Five LLM Tricks for Data Pipelines — A data-pipeline guide lists five tricks to stitch LLMs into workflows more smoothly. [4]
Local LLaMA: Provenance and Sources — A discussion about local LLMs asks if they can reveal sources or document provenance. The consensus leans toward retrieval-augmented generation at prompt time; some users mention pairing LM Studio with Brave search engine to fetch links live, or hardware like DGX Spark to push data in, while others note that local snapshots aren’t perfect stand-ins for the web. LocalLLaMA highlights the tension between on-device reasoning and source transparency. [5]
Provenance and prompt design will keep sparking debates as the field evolves.
References
Words that make language models perceive
Examines how prompt wording biases language models perceptions and outputs, exploring linguistic cues and prompt engineering effects
View sourceReasoning with Sampling: Your Base Model Is Smarter Than You Think
ArXiv paper argues base models' reasoning improves with sampling; challenges overestimating intelligence; implications for prompting and evaluation in practice today.
View sourcePPO for LLMs: A Guide for Normal People
Explains PPO in LLM tuning; practical guidance for non-experts on applying proximal policy optimization techniques and risks.
View sourceFive LLM Tricks for Data Pipelines
Discusses five techniques for using large language models in data pipelines, offering practical tips for integration and workflow optimization today.
View sourceCan local LLMs reveal sources/names of documents used to generate output?
Discusses citing sources, RAG use, local LLMs; compares GPT-5 variants, OSS-120B, Qwen 235B, browser tooling, and search APIs for sources.
View source